PPT-Query-Based Outlier Detection in Heterogeneous Information

Author : calandra-battersby | Published Date : 2016-10-31

Jonathan Kuck 1 Honglei Zhuang 1 Xifeng Yan 2 Hasan Cam 3 Jiawei Han 1 1 University of Illinois at UrbanaChampaign 2 University of California at Santa Barbara

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Query-Based Outlier Detection in Heterogeneous Information: Transcript

Jonathan Kuck 1 Honglei Zhuang 1 Xifeng Yan 2 Hasan Cam 3 Jiawei Han 1 1 University of Illinois at UrbanaChampaign 2 University of California at Santa Barbara 3 US Army Research Lab. Outlier Detection. Ayushi Dalmia. *. , Manish Gupta. *+. , Vasudeva Varma. *. 1. IIIT Hyderabad, India* Microsoft, India. +. Introduction. A. B. B. B. B. A. B. B. B. A. C. C. C. X. 1. Subgraphs from . Network Datasets. Manish . Gupta. UIUC. Microsoft. , India. Arun. . Mallya. , . Subhro. Roy. Jason Cho, Jiawei . Han. Motivation (1). Query based subgraph outlier detection. A security officer may like to find some tiny but . Gustavo Henrique Orair. Federal University of . Minas Gerais. Wagner Meira Jr.. Federal University of Minas Gerais. Presented by . Kajol. UH ID : 1358284. PURPOSE OF THE PAPER. Distance-Based . DASFAA 2011. By. Hoang Vu Nguyen, . Vivekanand. . Gopalkrishnan. and Ira . Assent. Presented By. Salman. Ahmed . Shaikh. (D1). Contents. Introduction. Subspace Outlier Detection Challenges. Objectives of Research. Sarah Riahi and Oliver Schulte. School . of Computing Science. Simon Fraser University. Vancouver, Canada. With tools that you probably have around the . house. lab.. A simple method for multi-relational outlier detection. Query-Based . Subnetwork Outliers in Heterogeneous . Information Networks. Honglei. Zhuang. 1. , Jing Zhang. 2. , George Brova. 1. , . Jie. Tang. 2. , Hasan Cam. 3. , . Xifeng. Yan. 4. , . Jiawei. Detection. Carolina . Ruiz. Department of Computer Science. WPI. Slides based on . Chapter 10 of. “Introduction to Data Mining”. textbook . by Tan, Steinbach, Kumar. (all figures and some slides taken from this chapter. Gustavo Henrique Orair. Federal University of . Minas Gerais. Wagner Meira Jr.. Federal University of Minas Gerais. Presented by . Kajol. UH ID : 1358284. PURPOSE OF THE PAPER. Distance-Based . Presented . by. Jeff . Bibeau. , Max Levine, . Jie. . Gao. Showcasing Work . by. . Milos . Hauskrecht. , . Iyad. . Batal. , Michal . Valko. , . Shyam. . Visweswaran. ,. Gregory F. Cooper, Gilles Clermont.. Outlier Detection. Ayushi Dalmia. *. , Manish Gupta. * . , Vasudeva Varma. *. 1. IIIT Hyderabad, India* Microsoft, India. . Introduction. A. B. B. B. B. A. B. B. B. A. C. C. C. X. 1. 9. Introduction to Data Mining, . 2. nd. Edition. by. Tan. , Steinbach, Karpatne, . Kumar. With additional slides and modifications by Carolina Ruiz, WPI. 11/20/2018. Introduction to Data Mining, 2nd Edition. Lecture Notes for Chapter 10. Introduction to Data Mining. by. Tan, Steinbach, Kumar. New slides have been added and the original slides have been significantly modified by . Christoph F. . Eick. Lecture Organization . “Anomaly Detection: A Tutorial”. Arindam. . Banerjee. , . Varun. . Chandola. , . Vipin. Kumar, Jaideep . Srivastava. , . University of Minnesota. Aleksandar. . Lazarevic. , . United Technology Research Center. 14. . World-Leading Research with Real-World Impact!. CS 5323. Outline. Anomaly detection. Facts and figures. Application. Challenges. Classification. Anomaly in Wireless. . 2. Recent News. Hacking of Government Computers Exposed 21.5 Million People.

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