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Anomaly Detection: Algorithms, Explanations, Applications
 
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Anomaly detection is important for data cleaning, cybersecurity, and robust AI systems. This talk will review recent work in our group on (a) benchmarking existing algorithms, (b) developing a theoretical understanding of their behavior, (c) explaining anomaly "alarms" to a data analyst, and (d) interactively re-ranking candidate anomalies in response to analyst feedback. Then the talk will describe two applications: (a) detecting and diagnosing sensor failures in weather networks and (b) open category detection in supervised learning. See more at https://www.microsoft.com/en-us/research/video/anomaly-detection-algorithms-explanations-applications/
Views: 8948 Microsoft Research
Machine Learning for Real-Time Anomaly Detection in Network Time-Series Data - Jaeseong Jeong
 
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Real-time anomaly detection plays a key role in ensuring that the network operation is under control, by taking actions on detected anomalies. In this talk, we discuss a problem of the real-time anomaly detection on a non-stationary (i.e., seasonal) time-series data of several network KPIs. We present two anomaly detection algorithms leveraging machine learning techniques, both of which are able to adaptively learn the underlying seasonal patterns in the data. Jaeseong Jeong is a researcher at Ericsson Research, Machine Learning team. His research interests include large-scale machine learning, telecom data analytics, human behavior predictions, and algorithms for mobile networks. He received the B.S., M.S., and Ph.D. degrees from Korea Advanced Institute of Science and Technology (KAIST) in 2008, 2010, and 2014, respectively.
Views: 12845 RISE SICS
Anomaly Detection in Telecommunications Using Complex Streaming Data | Whiteboard Walkthrough
 
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In this Whiteboard Walkthrough Ted Dunning, Chief Application Architect at MapR, explains in detail how to use streaming IoT sensor data from handsets and devices as well as cell tower data to detect strange anomalies. He takes us from best practices for data architecture, including the advantages of multi-master writes with MapR Streams, through analysis of the telecom data using clustering methods to discover normal and anomalous behaviors. For additional resources on anomaly detection and on streaming data: Download free pdf for the book Practical Machine Learning: A New Look at Anomaly Detection by Ted Dunning and Ellen Friedman https://www.mapr.com/practical-machine-learning-new-look-anomaly-detection Watch another of Ted’s Whiteboard Walkthrough videos “Key Requirements for Streaming Platforms: A Microservices Advantage” https://www.mapr.com/blog/key-requirements-streaming-platforms-micro-services-advantage-whiteboard-walkthrough-part-1 Read technical blog/tutorial “Getting Started with MapR Streams” sample programs by Tugdual Grall https://www.mapr.com/blog/getting-started-sample-programs-mapr-streams Download free pdf for the book Introduction to Apache Flink by Ellen Friedman and Ted Dunning https://www.mapr.com/introduction-to-apache-flink
Views: 4336 MapR Technologies
Nikunj Oza: "Data-driven Anomaly Detection" | Talks at Google
 
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This talk will describe recent work by the NASA Data Sciences Group on data-driven anomaly detection applied to air traffic control over Los Angeles, Denver, and New York. This data mining approach is designed to discover operationally significant flight anomalies, which were not pre-defined. These methods are complementary to traditional exceedance-based methods, in that they are more likely to yield false alarms, but they are also more likely to find previously-unknown anomalies. We discuss the discoveries that our algorithms have made that exceedance-based methods did not identify. Nikunj Oza is the leader of the Data Sciences Group at NASA Ames Research Center. He also leads a NASA project team which applies data mining to aviation safety. Dr. Ozaąs 40+ research papers represent his research interests which include data mining, machine learning, anomaly detection, and their applications to Aeronautics and Earth Science. He received the Arch T. Colwell Award for co-authoring one of the five most innovative technical papers selected from 3300+ SAE technical papers in 2005. His data mining team received the 2010 NASA Aeronautics Research Mission Directorate Associate Administratorąs Award for best technology achievements by a team. He received his B.S. in Mathematics with Computer Science from MIT in 1994, and M.S. (in 1998) and Ph.D. (in 2001) in Computer Science from the University of California at Berkeley.
Views: 7696 Talks at Google
SpotLight: Detecting Anomalies in Streaming Graphs
 
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Authors: Dhivya Eswaran (Carnegie Mellon University); Christos Faloutsos (Carnegie Mellon University); Sudipto Guha (Amazon); Nina Mishra (Amazon) More on http://www.kdd.org/kdd2018/
Views: 529 KDD2018 video
What is ANOMALY DETECTION? What does ANOMALY DETECTION mean? ANOMALY DETECTION meaning
 
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What is ANOMALY DETECTION? What does ANOMALY DETECTION mean? ANOMALY DETECTION meaning - ANOMALY DETECTION definition - ANOMALY DETECTION explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. In data mining, anomaly detection (also outlier detection) is the identification of items, events or observations which do not conform to an expected pattern or other items in a dataset.[1] Typically the anomalous items will translate to some kind of problem such as bank fraud, a structural defect, medical problems or errors in a text. Anomalies are also referred to as outliers, novelties, noise, deviations and exceptions.[2] In particular in the context of abuse and network intrusion detection, the interesting objects are often not rare objects, but unexpected bursts in activity. This pattern does not adhere to the common statistical definition of an outlier as a rare object, and many outlier detection methods (in particular unsupervised methods) will fail on such data, unless it has been aggregated appropriately. Instead, a cluster analysis algorithm may be able to detect the micro clusters formed by these patterns.[3] Three broad categories of anomaly detection techniques exist.[1] Unsupervised anomaly detection techniques detect anomalies in an unlabeled test data set under the assumption that the majority of the instances in the data set are normal by looking for instances that seem to fit least to the remainder of the data set. Supervised anomaly detection techniques require a data set that has been labeled as "normal" and "abnormal" and involves training a classifier (the key difference to many other statistical classification problems is the inherent unbalanced nature of outlier detection). Semi-supervised anomaly detection techniques construct a model representing normal behavior from a given normal training data set, and then testing the likelihood of a test instance to be generated by the learnt model.
Views: 4938 The Audiopedia
Anomaly Detection in Fuzzy Clustering on streaming data
 
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Left window shows where anomaly has been detected by setting a tolerance on membership of the clusters. Right window shows where the anomalies shift the cluster centroids.
Views: 10 Suraj Pattar
Lecture 15.1 — Anomaly Detection Problem | Motivation  — [ Machine Learning | Andrew Ng ]
 
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. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. .
Anomaly Detection for Data Quality and Metric Shifts at Netflix | DataEngConf SF '17
 
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Don’t miss the next DataEngConf in Barcelona: https://dataeng.co/2O0ZUq7 Recorded at DataEngConf SF17 in April, 2017 In the course of transforming, publishing and visualizing data, there’s risk of “bad data” creeping into your output at every turn, hurting data credibility and distracting teams from investigating real metric shifts. How does Netflix prevent bad data from causing bad decision-making? We use a variety of techniques to automate the basics, allowing us to focus our energy on the changes in data that indicate real problems with the Netflix product. Hear examples of 1) the checks we impose at multiple steps of the data pipeline to identify source data quality issues and business metric shifts, 2) techniques for anomaly detection on datasets with many dimensions that are highly cardinal, 3) how to set up evaluations in an automated fashion and 4) how we make it easy for humans to investigate issues.
Views: 1718 Hakka Labs
Distributed Local Outlier Detection in Big Data
 
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Distributed Local Outlier Detection in Big Data Yizhou Yan (Worcester Polytechnic Institute) Lei Cao (Massachusetts Institute of Technology) Caitlin Kuhlman (Worcester Polytechnic Institute) Elke Rundensteiner (Worcester Polytechnic Institute) In this work, we present the first distributed solution for the Local Outlier Factor (LOF) method—a popular outlier detection technique shown to be very effective for datasets with skewed distributions. As datasets increase radically in size, highly scalable LOF algorithms leveraging modern distributed infrastructures are required. This poses significant challenges due to the complexity of the LOF definition, and a lack of access to the entire dataset at any individual compute machine. Our solution features a distributed LOF pipeline framework, called DLOF. Each stage of the LOF computation is conducted in a fully distributed fashion by leveraging our invariant observation for intermediate value management. Furthermore, we propose a data assignment strategy which ensures that each machine is self-sufficient in all stages of the LOF pipeline, while minimizing the number of data replicas. Based on the convergence property derived from analyzing this strategy in the context of real world datasets, we introduce a number of data-driven optimization strategies. These strategies not only minimize the computation costs within each stage, but also eliminate unnecessary communication costs by aggressively pushing the LOF computation into the early stages of the DLOF pipeline. Our comprehensive experimental study using both real and synthetic datasets confirms the efficiency and scalability of our approach to terabyte level data. More on http://www.kdd.org/kdd2017/
Views: 1494 KDD2017 video
Unsupervised Anomaly Detection With Advanced Analytics: Your Next Steps - Harizo Rajaona
 
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How can we improve anomaly detection with unsupervised methods? After a quick sum up of supervised methods, we'll show how advanced machine learning can leverage the power of algorithms. We'll conclude with a few use case applications from different customers. #HyperightDataTalks is a video podcast of best presentations, discussions and interviews with some of the most innovative minds, enterprise practitioners, technology and service providers, start-ups and academics, working with Data Science, Data Management, Big Data, Analytics, AI, IOT and much more. All presentations are taken from Hyperight´s Data summits and now available for you. For more interviews, audio podcast and videos from some of the best presentations from our Data Summits, please visit http://www.hyperight.com Presentation recorded during: Nordic Data Science and Machine Learning Summit 2017 - http://www.nordicdatasciencesummit.com/ Follow us on Twitter: https://Twitter.com/datasweden More information about Hyperight: http://www.hyperight.com/ Subscribe to our channel: https://www.youtube.com/channel/UCCLYBm1MHI3jIvZo9YKPq-g
Views: 827 Hyperight AB
Fast Memory-Efficient Anomaly Detection in Streaming Heterogenous Graphs (KDD 2016)
 
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Promo video for StreamSpot: http://bit.lty/streamspot
Views: 102 emaadmanzoor
Outlier Detection/Removal Algorithm
 
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This video is part of an online course, Intro to Machine Learning. Check out the course here: https://www.udacity.com/course/ud120. This course was designed as part of a program to help you and others become a Data Analyst. You can check out the full details of the program here: https://www.udacity.com/course/nd002.
Views: 13218 Udacity
Unsupervised Data Center Health Monitoring and Anomaly Detection
 
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Moshe Gabel - Department of Computer Science at the Technion, Haifa, Israel Modern data centers are comprised of hundreds or thousands of machines (or more!). With so many machines, failures are commonplace, so failure detection is crucial: undetected failures may lead to data loss and outages. Traditional fault detection techniques are often supervised, relying on domain knowledge and precious (often unavailable) training data, and are inflexible. More recent approaches focus on early detection and handling of performance problems, or latent faults. These faults "fly under the radar" of existing detection systems because they are not acute enough, or were not anticipated by maintenance engineers. This talk will review ongoing work on unsupervised fault detection in large scale data centers, such as those used cloud services, supercomputers, and compute clusters. We will first discuss unsupervised latent fault detection in scale-out, load-balanced cloud services. I'll present a novel framework for statistical latent fault detection using only ordinary machine counters collected as standard practice, and demonstrate three detection methods within this framework. Derived tests are adaptive, domain-independent and unsupervised, require neither background information nor tuning, and scale to very large services. We proved strong guarantees on the false positive rates of our tests. Our evaluation on a large, real-world production service shows that at least 20% of machine or software failures were preceded by such latent fault. We further show that our latent fault detector can anticipate failures up to 14 days ahead, with high precision and very low FPR. Time allowing, I will then briefly present some extensions of this work. The first is a communication-efficient variant designed for online outlier detection in distributed data streams. Using stream processing techniques that trade accuracy for communication and computation, the adapted latent fault detector can reduce bandwidth costs by an order of magnitude with below 1% error compared to the original algorithm. The second, is a latent fault detector for unbalanced workloads, such as map-reduce jobs and compute clusters. This new scheme, based on Principal Components Analysis, retains the advantages of the previous methods: it is unsupervised, robust to changes, and statistically sound. Preliminary evaluation on supercomputer logs shows that the new method is able to correctly predict some failures, while our previous methods completely fail in this setting. Preliminary evaluation also shows good performance on virtual machines running Hadoop and CassandraDB. We'll also touch on another scheme for opaque virtual machines, based on a sparse decomposition approach.
Views: 137 Datamininguba
Time series anomaly detection in real time.
 
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This shows an example of real-time time series anomaly discovery with rule density curve built using sliding window-based SAX discretization and grammatical inference with Sequitur. Our paper describing the approach: http://csdl.ics.hawaii.edu/techreports/2014/14-05/14-05.pdf (SAX parameters used: window 400, PAA size 8, Alphabet size 6)
Views: 4360 seninp
Science of Anomaly Detection
 
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"Science of Anomaly Detection" Video Talk (17:08) Scott Purdy Engineering Manager, Numenta Numenta Workshop October 17, 2014 Redwood City, CA
Views: 11993 Numenta
027 Anomaly detection in R
 
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Data Science Foundations: Data Mining http://bc.vc/jSMxfA3
Views: 3624 Tukang Leding
Time Series data Mining Using the Matrix Profile part 1
 
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Time Series data Mining Using the Matrix Profile: A Unifying View of Motif Discovery, Anomaly Detection, Segmentation, Classification, Clustering and Similarity Joins Part 1 Authors: Abdullah Al Mueen, Department of Computer Science, University of New Mexico Eamonn Keogh, Department of Computer Science and Engineering, University of California, Riverside Abstract: The Matrix Profile (and the algorithms to compute it: STAMP, STAMPI, STOMP, SCRIMP and GPU-STOMP), has the potential to revolutionize time series data mining because of its generality, versatility, simplicity and scalability. In particular it has implications for time series motif discovery, time series joins, shapelet discovery (classification), density estimation, semantic segmentation, visualization, clustering etc. Link to tutorial: http://www.cs.ucr.edu/~eamonn/MatrixProfile.html More on http://www.kdd.org/kdd2017/ KDD2017 Conference is published on http://videolectures.net/
Views: 1848 KDD2017 video
Anomaly Detection Example
 
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This video is part of the Udacity course "Intro to Information Security". Watch the full course at https://www.udacity.com/course/ud459
Views: 1716 Udacity
Anomaly Detection 101 - Elizabeth (Betsy) Nichols Ph.D.
 
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This presentation surveys a collection of techniques for detecting anomalies in a DevOps environment. Each of the techniques has strengths and weaknesses that are illustrated via real-world (anonymized) customer data. Techniques discussed include deterministic and statistical models as well as uni-variate and multi-variate analytics. Examples are given that show concrete evidence where each can succeed and each can fail. This presentation is about concepts and how to think about alternative anomaly detection techniques. This presentation is not an academic discourse in math, statistics or probability theory. Elizabeth A. Nichols (Betsy) is Chief Data Scientist at Netuitive, Inc. In this role she is responsible for leading the company's vision and technologies for analytics, modeling, and algorithms. Betsy has applied mathematics and computer technologies to create systems for war gaming, space craft mission optimization, industrial process control, supply chain logistics, electronic trading, advertising networks, IT security and risk models, and network and systems management. She has co-founded three companies, all of which delivered analytics to commercial and government enterprises. Betsy graduated with an A.B. from Vassar College and a Ph.D. in Mathematics from Duke University. Check her out on LinkedIn (https://www.linkedin.com/in/elizabethanichols) for more information.
Needle in the Haystack—User Behavior Anomaly Detection for Information Security
 
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Salesforce recently invented and deployed a real-time, scalable, terabyte data-level and low false positive personalized anomaly detection system. Anomaly detection on user in-app behavior at terabyte-data scale is extremely challenging because traditional techniques like clustering methods suffer serious production performance issues. With Ping Yan and Wei Dang.
Views: 1180 Databricks
xStream: Outlier Detection in Feature-Evolving Data Streams
 
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Authors: Emaad Manzoor (CMU), Hemank Lamba (CMU), Leman Akoglu (CMU) Abstract: This work addresses the outlier detection problem for feature-evolving streams, which has not been studied before. In this setting both (1) data points may evolve, with feature values changing, as well as (2) feature space may evolve, with newly-emerging features over time. This is notably different from row-streams, where points with fixed features arrive one at a time. We propose a density-based ensemble outlier detector, called xStream, for this more extreme streaming setting which has the following key properties: (1) it is a constant-space and constant-time (per incoming update) algorithm, (2) it measures outlierness at multiple scales or granularities, it can handle (3i) high-dimensionality through distance-preserving projections, and (3ii) non-stationarity via O(1)-time model updates as the stream progresses. In addition, xStream can address the outlier detection problem for the (less general) disk-resident static as well as row-streaming settings. We evaluate xStream rigorously on numerous real-life datasets in all three settings: static, row-stream, and feature-evolving stream. Experiments under static and row-streaming scenarios show that xStream is as competitive as state-of-the-art detectors and particularly effective in high-dimensions with noise. We also demonstrate that our solution is fast and accurate with modest space overhead for evolving streams, on which there exists no competition. More on http://www.kdd.org/kdd2018/
Views: 214 KDD2018 video
Finding Outliers in Streaming Data: A Scalable Approach (Casey Stella)
 
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Detecting outliers and anomalies in data is one of the most common tasks that the working data scientist is asked to do. This is especially common and extra challenging with fast streaming data coming from many IoT sources. Despite this, the library support for problems of this variety are woefully unavailable. Often data scientists are forced to go to research papers and implement their own solutions. This talk will cover using the Spark Streaming coupled with a novel new algorithmic approach to detecting outliers at scale using a composition of distributional sketches as well as more classical techniques along with off-the-shelf UI components to demonstrate how this common but challenging task might be accomplished with for IoT data as well as more traditional streaming data.
Views: 812 Spark Summit
Anomaly Detection in Streams with Extreme Value Theory
 
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Anomaly Detection in Streams with Extreme Value Theory Alban Siffer (IRISA) Pierre-Alain Fouque (IRISA) Alexandre Termier (IRISA) Christine Largouët (IRISA) Anomaly detection in time series has attracted considerable attention due to its importance in many real-world applications including intrusion detection, energy management and finance. Most approaches for detecting outliers rely on either manually set thresholds or assumptions on the distribution of data according to Chandola, Banerjee and Kumar. Here, we propose a new approach to detect outliers in streaming univariate time series based on Extreme Value Theory that does not require to hand-set thresholds and makes no assumption on the distribution: the main parameter is only the risk, controlling the number of false positives. Our approach can be used for outlier detection, but more generally for automatically setting thresholds, making it useful in wide number of situations. We also experiment our algorithms on various real-world datasets which confirm its soundness and efficiency. More on http://www.kdd.org/kdd2017/
Views: 931 KDD2017 video
Fraud and Anomaly Detection using Oracle Advanced Anlaytics Part 2 Demo
 
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This is Part 2 of my Fraud and Anomaly Detection using Oracle Advanced Anlaytics YouTube posting a few days ago.
Views: 3100 Charles Berger
Generic and Scalable Framework for Automated Time-series Anomaly Detection
 
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Authors: Nikolay Laptev, Saeed Amizadeh, Ian Flint Abstract: This paper introduces a generic and scalable framework for automated anomaly detection on large scale time-series data. Early detection of anomalies plays a key role in maintaining consistency of person's data and protects corporations against malicious attackers. Current state of the art anomaly detection approaches suffer from scalability, use-case restrictions, difficulty of use and a large number of false positives. Our system at Yahoo, EGADS, uses a collection of anomaly detection and forecasting models with an anomaly filtering layer for accurate and scalable anomaly detection on time-series. We compare our approach against other anomaly detection systems on real and synthetic data with varying time-series characteristics. We found that our framework allows for 50-60% improvement in precision and recall for a variety of use-cases. Both the data and the framework are being open-sourced. The open-sourcing of the data, in particular, represents the first of its kind effort to establish the standard benchmark for anomaly detection. ACM DL: http://dl.acm.org/citation.cfm?id=2788611 DOI: http://dx.doi.org/10.1145/2783258.2788611
Outlier Detection using Orange and Chicago Homicide Data
 
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I made this video to show some of the workflow of outlier detection using Orange machine learning platform and CartoDB for mapping the data. The source data was pulled from Chicago's public dataset. flagshipdynamics.blogspot.com
Views: 1181 Brandon Pippin
Fast Memory-efficient Anomaly Detection in Streaming Heterogeneous Gr.. (KDD 2016)
 
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Fast Memory-efficient Anomaly Detection in Streaming Heterogeneous Graphs KDD 2016 Emaad Manzoor Sadegh M. Milajerdi Leman Akoglu Given a stream of heterogeneous graphs containing different types of nodes and edges, how can we spot anomalous ones in real-time while consuming bounded memory? This problem is motivated by and generalizes from its application in security to host-level advanced persistent threat (APT) detection. We propose StreamSpot, a clustering based anomaly detection approach that addresses challenges in two key fronts: (1) heterogeneity, and (2) streaming nature. We introduce a new similarity function for heterogeneous graphs that compares two graphs based on their relative frequency of local substructures, represented as short strings. This function lends itself to a vector representation of a graph, which is (a) fast to compute, and (b) amenable to a sketched version with bounded size that preserves similarity. StreamSpot exhibits desirable properties that a streaming application requires: it is (i) fully-streaming; processing the stream one edge at a time as it arrives, (ii) memory-efficient; requiring constant space for the sketches and the clustering, (iii) fast; taking constant time to update the graph sketches and the cluster summaries that can process over 100,000 edges per second, and (iv) online; scoring and flagging anomalies in real time. Experiments on datasets containing simulated system-call flow graphs from normal browser activity and various attack scenarios (ground truth) show that StreamSpot is high-performance; achieving above 95% detection accuracy with small delay, as well as competitive time and memory usage.
Adaptive Graph-Based Algorithms for Online Semi-Supervised Learning & Conditional Anomaly Detection
 
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We present graph-based methods for online semi-supervised learning and conditional anomaly detection. When data arrive in a stream, the problems of computation and data storage arise for any graph-based method. We propose a fast approximate online algorithm that solves for the harmonic solution on an approximate graph. We show, both empirically and theoretically, that good behavior can be achieved by collapsing nearby points into a set of local representative points that minimize distortion. Moreover, we regularize the harmonic solution to achieve better stability properties. We also present a graph-based method for detecting conditional outliers and apply it to the identification of unusual outcomes and patient-management decisions. Our hypothesis is that patient-management decisions that are unusual with respect to past patients may be due to errors and that it is worthwhile to raise an alert if such a condition is encountered. Conditional anomaly detection extends standard unconditional anomaly framework but also faces new problems known as fringe points and unconditional anomalies. We present an extensive human evaluation study of our methods by 15 experts in critical care.
Views: 811 Microsoft Research
Detecting Network Intrusions With Machine Learning Based Anomaly Detection Techniques
 
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Machine learning techniques used in network intrusion detection are susceptible to “model poisoning” by attackers. The speaker will dissect this attack, analyze some proposals for how to circumvent such attacks, and then consider specific use cases of how machine learning and anomaly detection can be used in the web security context. Author: Clarence Chio More: http://www.phdays.com/program/tech/40866/
Views: 9385 Positive Technologies
SAP HANA Academy - Streaming Analytics: Clustering - DenStream Model [1.0 SP 11]
 
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In this video tutorial, learn how to build a clustering and outlier detection model using the streaming machine learning capabilities in SAP HANA streaming analytics. The DenStream algorithm discovers data object clusters of arbitrary shape, handles data outliers and noise, and by pruning, maintains only necessary information for data clustering to limit memory consumption. The statistical properties of the incoming streaming data change over time, and in unforeseen ways. To remain accurate, the DenStream algorithm runs constant updates. Old data or clusters are less important, and outliers may evolve into clusters. We cannot keep all the data points from the stream, so instead we form core-micro-clusters by first identifying potential core-micro-clusters and outlier-micro-clusters. To access the code snippets used in the video series, please visit https://github.com/saphanaacademy/SDS. CONNECT WITH US Feel free to connect with us at the links below: LinkedIn: https://linkedin.com/in/saphanaacademy Twitter: https://twitter.com/saphanaacademy Facebook: https://www.facebook.com/saphanaacademy/ Google+: https://plus.google.com/+saphanaacademy Github: https://github.com/saphanaacademy Thank you for watching. Video by the SAP HANA Academy.
Views: 281 SAP HANA Academy
Details of Anomaly Detection in Big Data, Nikunj Oza, 20140728
 
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Nikunj Oza, Leader of the Data Sciences Group, NASA Ames Research Center Joint Event with Hadoop Talks Meetup Data-driven methods for anomaly detection identifies as anomalies those data points that do not fit with most of the data in some sense. For example, the anomalies may have greater distances to their nearest neighbors or lower probabilities with respect to an appropriate probability model. However, measuring distances between points or probabilities of points is problematic when working with "big data," with their heterogeneity and volume. In this talk, I will describe the problem in more detail, the heterogeneous data sources available to us, the methods we use to leverage these data sources, and the general data management and data mining problems that we need to solve moving forward. Speaker Bio Nikunj Oza is the leader of the Data Sciences Group at NASA Ames Research Center. He also leads the Discovery of Precursors to Safety Incidents (DPSI) team which applies data mining to aviation safety. Dr. Oza’s 40+ research papers represent his research interests which include data mining, machine learning, anomaly detection, and their applications to Aeronautics and Earth Science. He received the Arch T. Colwell Award for co-authoring one of the five most innovative technical papers selected from 3300+ SAE technical papers in 2005. His DPSI team received the 2010 NASA Aeronautics Research Mission Directorate Associate Administrator’s Award for best technology achievements by a team. He received his B.S. in Mathematics with Computer Science from MIT in 1994, and M.S. (in 1998) and Ph.D. (in 2001) in Computer Science from the University of California at Berkeley. http://www.meetup.com/SF-Bay-ACM/events/183069232/ http://www.sfbayacm.org/event/hadoop-talk-details-anomaly-detection-big-data
Views: 1409 San Francisco Bay ACM
Discovering via Link Anomaly Detection Emerging Topics in Social Streams
 
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Views: 30 Raghunath M
Discovering Emerging Topics in Social Streams via Link-Anomaly Detection
 
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Final year data mining projects, Ieee Projects in bangalore, chennai Data mining Big data projects
Views: 25 Jothiramalingam K
DBSCAN Clustering for Identifying Outliers Using Python - Tutorial 22 in Jupyter Notebook
 
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In this tutorial about python for data science, you will learn about DBSCAN (Density-based spatial clustering of applications with noise) Clustering method to identify/ detect outliers in python. you will learn how to use two important DBSCAN model parameters i.e. Eps and min_samples. Environment used for coding is Jupyter notebook. (Anaconda) This is the 22th Video of Python for Data Science Course! In This series I will explain to you Python and Data Science all the time! It is a deep rooted fact, Python is the best programming language for data analysis because of its libraries for manipulating, storing, and gaining understanding from data. Watch this video to learn about the language that make Python the data science powerhouse. Jupyter Notebooks have become very popular in the last few years, and for good reason. They allow you to create and share documents that contain live code, equations, visualizations and markdown text. This can all be run from directly in the browser. It is an essential tool to learn if you are getting started in Data Science, but will also have tons of benefits outside of that field. Harvard Business Review named data scientist "the sexiest job of the 21st century." Python pandas is a commonly-used tool in the industry to easily and professionally clean, analyze, and visualize data of varying sizes and types. We'll learn how to use pandas, Scipy, Sci-kit learn and matplotlib tools to extract meaningful insights and recommendations from real-world datasets. Download Link for Cars Data Set: https://www.4shared.com/s/fWRwKoPDaei Download Link for Enrollment Forecast: https://www.4shared.com/s/fz7QqHUivca Download Link for Iris Data Set: https://www.4shared.com/s/f2LIihSMUei https://www.4shared.com/s/fpnGCDSl0ei Download Link for Snow Inventory: https://www.4shared.com/s/fjUlUogqqei Download Link for Super Store Sales: https://www.4shared.com/s/f58VakVuFca Download Link for States: https://www.4shared.com/s/fvepo3gOAei Download Link for Spam-base Data Base: https://www.4shared.com/s/fq6ImfShUca Download Link for Parsed Data: https://www.4shared.com/s/fFVxFjzm_ca Download Link for HTML File: https://www.4shared.com/s/ftPVgKp2Lca
Views: 8595 TheEngineeringWorld
Signal Processing and Machine Learning Techniques for Sensor Data Analytics
 
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Free MATLAB Trial: https://goo.gl/yXuXnS Request a Quote: https://goo.gl/wNKDSg Contact Us: https://goo.gl/RjJAkE Learn more about MATLAB: https://goo.gl/8QV7ZZ Learn more about Simulink: https://goo.gl/nqnbLe ------------------------------------------------------------------------- An increasing number of applications require the joint use of signal processing and machine learning techniques on time series and sensor data. MATLAB can accelerate the development of data analytics and sensor processing systems by providing a full range of modelling and design capabilities within a single environment. In this webinar we present an example of a classification system able to identify the physical activity that a human subject is engaged in, solely based on the accelerometer signals generated by his or her smartphone. We introduce common signal processing methods in MATLAB (including digital filtering and frequency-domain analysis) that help extract descripting features from raw waveforms, and we show how parallel computing can accelerate the processing of large datasets. We then discuss how to explore and test different classification algorithms (such as decision trees, support vector machines, or neural networks) both programmatically and interactively. Finally, we demonstrate the use of automatic C/C++ code generation from MATLAB to deploy a streaming classification algorithm for embedded sensor analytics.
Views: 11980 MATLAB
Anomaly Detection - Density Estimation - Algorithm
 
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Anomaly Detection Andrew Ng Hello all! I hope everyone has been enjoying the course and learning a lot! In this module, we will be covering anomaly detection which is widely used in fraud detection (e.g. ‘has this credit card been stolen?’). Given a large number of data points, we may sometimes want to figure out which ones vary significantly from the average. For example, in manufacturing, we may want to detect defects or anomalies. We show how a dataset can be modeled using a Gaussian distribution, and how the model can be used for anomaly detection. We will also be covering recommender systems, which are used by companies like Amazon, Netflix and Apple to recommend products to their users. Recommender systems look at patterns of activities between different users and different products to produce these recommendations. In these lessons, we introduce recommender algorithms such as the collaborative filtering algorithm and low-rank matrix factorization.
Views: 303 intrigano
2014 IEEE DATA MINING Discovering Emerging Topics in Social Streams via Link Anomaly Detection
 
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To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - [email protected] Our Website: www.globalsofttechnologies.org
Pre-processing & Anomaly Detection API for log data
 
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I created this video with the YouTube Video Editor (http://www.youtube.com/editor)
Views: 214 shioin
#bbuzz 2015: Andrew Clegg - Signatures, patterns and trends: Timeseries data mining at Etsy
 
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Find more information here: http://berlinbuzzwords.de/session/signatures-patterns-and-trends-timeseries-data-mining-etsy Etsy loves metrics. Everything that happens in our data centres gets recorded, graphed and stored. But with over a million metrics flowing in constantly, it’s hard for any team to keep on top of all that information. Graphing everything doesn’t scale, and traditional alerting methods based on thresholds become very prone to false positives. That’s why we started Kale, an open-source software suite for pattern mining and anomaly detection in operational data streams. These are big topics with decades of research, but many of the methods in the literature are ineffective on terabytes of noisy data with unusual statistical characteristics, and techniques that require extensive manual analysis are unsuitable when your ops teams have service levels to maintain. In this talk I’ll briefly cover the main challenges that traditional statistical methods face in this environment, and introduce some pragmatic alternatives that scale well and are easy to implement (and automate) on Elasticsearch and similar platforms. I’ll talk about the stumbling blocks we encountered with the first release of Kale, and the resulting architectural changes coming in version 2.0. And I’ll go into a little technical detail on the algorithms we use for fingerprinting and searching metrics, and detecting different kinds of unusual activity. These techniques have potential applications in clustering, outlier detection, similarity search and supervised learning, and they are not limited to the data centre but can be applied to any high-volume timeseries data. Kale version 1 is described here: https://codeascraft.com/2013/06/11/introducing-kale/ Version 2 has the same goals but a very different architecture and suite of tools. Come along if you'd like to learn more.
Prelert machine learning anomaly detection for IT Ops
 
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Automate data analysis and detect problems early so that you can act fast! http://www.prelert.com
Views: 681 Prelert
Discovering Emerging Topics in Social Streams via Link-Anomaly Detection
 
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To get this project in ONLINE or through TRAINING Sessions, Contact:JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83. Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai, Thattanchavady, Puducherry -9. Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690 , Email: [email protected], web: www.jpinfotech.org Blog: www.jpinfotech.blogspot.com Discovering Emerging Topics in Social Streams via Link-Anomaly Detection using JAVA. We have used Real time Twitter data and Twitter account with Trends and Tweets made.
Views: 930 jpinfotechprojects
Machine Learning Tutorial 15 - Outliers
 
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Best Machine Learning book: https://amzn.to/2MilWH0 (Fundamentals Of Machine Learning for Predictive Data Analytics). Machine Learning and Predictive Analytics. #MachineLearning One of the processes in machine learning is data cleaning. This video deals specifically with the problems that outliers cause. They mess up our data visualization and our measures of central tendency. This online course covers big data analytics stages using machine learning and predictive analytics. Big data and predictive analytics is one of the most popular applications of machine learning and is foundational to getting deeper insights from data. Starting off, this course will cover machine learning algorithms, supervised learning, data planning, data cleaning, data visualization, models, and more. This self paced series is perfect if you are pursuing an online computer science degree, online data science degree, online artificial intelligence degree, or if you just want to get more machine learning experience. Enjoy! Check out the entire series here: https://www.youtube.com/playlist?list=PL_c9BZzLwBRIPaKlO5huuWQdcM3iYqF2w&playnext=1 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Support me! http://www.patreon.com/calebcurry Subscribe to my newsletter: http://bit.ly/JoinCCNewsletter Donate!: http://bit.ly/DonateCTVM2. ~~~~~~~~~~~~~~~Additional Links~~~~~~~~~~~~~~~ More content: http://CalebCurry.com Facebook: http://www.facebook.com/CalebTheVideoMaker Google+: https://plus.google.com/+CalebTheVideoMaker2 Twitter: http://twitter.com/calebCurry Amazing Web Hosting - http://bit.ly/ccbluehost (The best web hosting for a cheap price!)
Views: 1028 Caleb Curry
Build A Complete Project In Machine Learning | Credit Card Fraud Detection | Eduonix
 
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Look what we have for you! Another complete project in Machine Learning! In today's tutorial, we will be building a Credit Card Fraud Detection System from scratch! It is going to be a very interesting project to learn! It is one of the 10 projects from our course 'Projects in Machine Learning' which is currently running on Kickstarter. For this project, we will be using the several methods of Anomaly detection with Probability Densities. We will be implementing the two major algorithms namely, 1. A local out wire factor to calculate anomaly scores. 2. Isolation forced algorithm. To get started we will first build a dataset of over 280,000 credit card transactions to work on! You can access the source code of this tutorial here: https://github.com/eduonix/creditcardML Early Black Friday Sale is here!! Get the premium courses starting at just $5. Check out the courses here: http://bit.ly/2OFHWZa Don't forget to check our new project on Data Science Foundational Program on Kickstarter. This program incorporates everything from beginner-level concepts to real-world implementation along with 4 courses, 2 e-books, Interview preparation guide, multiple labs, numerous practice tests and much more. Read more - https://kck.st/2CuIkay Thank you for watching! We’d love to know your thoughts in the comments section below. Also, don’t forget to hit the ‘like’ button and ‘subscribe’ to ‘Eduonix Learning Solutions’ for regular updates. https://goo.gl/BCmVLG Follow Eduonix on other social networks: ■ Facebook: http://bit.ly/2nL2p59 ■ Linkedin: http://bit.ly/2nKWhKa ■ Instagram: http://bit.ly/2nL8TRu | @eduonix ■ Twitter: http://bit.ly/2eKnxq8
Dynamic Outlier Detection in StreamSets Data Collector
 
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Demonstration of dynamic outlier detection - StreamSets filters anomalous values off to a file for analysis.
Views: 250 Pat Patterson
Living on the Fringe: Outlier Detection in the Age of Data
 
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Speaker: Kelly M. Kirtland Thursday, April 10, 2014

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