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Time Series Epenthesis: Clustering Time Series Streams Requires Ignoring Some Data Thanawin Rakthanmanon Eamonn Keogh Stefano Lonardi Scott Evans

Text Similarity Dr Eamonn Keogh Computer Science & Engineering Department University of California - Riverside Riverside,CA 92521 [email protected]

SAX: a Novel Symbolic Representation of Time Series Authors Jessica Lin Eamonn Keogh Li Wei Stefano Lonardi Presenter Arif Bin Hossain Slides incorporate

Using Relevance Feedback in Multimedia Databases Chotirat “Ann” Ratanamahatana Eamonn Keogh 7 th International Conference on VISual Information Systems

Chapter 1 MINING TIME SERIES DATAjessica/BookChapterTSMining.pdfChapter 1 MINING TIME SERIES DATA Chotirat Ann Ratanamahatana, Jessica Lin, Dimitrios Gunopulos, Eamonn Keogh University

Instruction Set Extension for Dynamic Time Warping Joseph Tarango, Eammon Keogh, Philip Brisk {jtarango,eamonn,philip}@cs.ucr.edu {jtarango,eamonn,philip}

Eamonn Keogh

UNIVERSITY OF JYVÄSKYLÄ DEPARTMENT OF MATHEMATICAL ...mpechen/courses/TIES443/handouts/lecture… · (c) Eamonn Keogh, [email protected] UNIVERSITY OF JYVÄSKYLÄ DEPARTMENT OF

Exact Indexing of Dynamic Time Warping - VLDBExact Indexing of Dynamic Time Warping Eamonn Keogh University of California - Riverside Computer Science & Engineering Department Riverside,

Data Mining Eamonn Keogh. What is data mining? Generally, data mining (sometimes called data or knowledge discovery) is the process of analyzing data

Human computation Gesture CAPTCHA Jaehoon Kim Committees : Eamonn Keogh, Stefano Lonardi

Slides at eamonn/public/cs170guest.ppt Eamonn Keogh [email protected]

Accelerating the Dynamic Time Warping Distance Measure Using Logarithmic Arithmetic Joseph Tarango, Eamonn Keogh, Philip Brisk {jtarango,eamonn,philip}@cs.ucr.edu

Visualizing and Discovering Nontrivial Patterns In Large Time Series Databases Jessica Lin, Eamonn Keogh, Stefano Loardi Information Visualization 2005,

A Compression Based Distance Measure for Texture Bilson J. L. Campana Eamonn J. Keogh University of California – Riverside [email protected] 1

SAX! A Symbolic Representations of Time Series Eamonn Keogh and Jessica Lin

Making Time-series Classification More Accurate Using Learned Constraints © Chotirat “Ann” Ratanamahatana Eamonn Keogh 2004 SIAM International Conference

Spatial Access Methods Chapter 26 of book Read only 26.1, 26.2, 26.6 Dr Eamonn Keogh Computer Science & Engineering Department University of California

Interpreting and Explaining Deep Neural Networks: A Perspective …xai.kaist.ac.kr/static/files/2020tutorial/XAI_KDD... · 2020. 8. 23. · Slides courtesy of Eamonn Keogh The dataset

User Manual of Mining Mouse Vocalizations Prepared by Jesin Zakaria and Eamonn Keogh

Symbolic Representations of Time Series Eamonn Keogh and Jessica Lin Computer Science & Engineering Department University of California - Riverside Riverside,CA

CS 235 Data Mining Winter 2011 Eamonn Keogh UCR TA: Abdullah Mueen

User manual of MK Prepared by Abdullah Mueen and Eamonn Keogh

Dendrograms for Data Mining Eamonn Keogh. Informally, finding natural groupings or relationships among objects. What is Clustering? Also called unsupervised

Time Series Data Mining Group Disk Aware Discord Discovery: Finding Unusual Time Series in Terabyte Sized Datasets Dragomir Yankov, Eamonn Keogh, Computer

Symbolic Representations of Time Series Eamonn Keogh and Jessica Lin

Accelerating Dynamic Time Warping Clustering with a Novel Admissible Pruning Strategy Nurjahan BegumLiudmila Ulanova Jun Wang 1 Eamonn Keogh University

CS 170 INTRO TO ARTIFICIAL INTELLIGENCE CS 170 INTRO TO ARTIFICIAL INTELLIGENCE Dr Eamonn Keogh [email protected] eamonn/ Office:

Measurement of Similarity and Clustering Dr Eamonn Keogh Computer Science & Engineering Department University of California - Riverside Riverside,CA 92521

Eamonn Keogh What can Computer Science do for Malaria Research? Computer Science & Engineering Department University of California - Riverside Riverside,

Problem Motivation - UCR Computer Science and Engineeringameadows/msproject/slides-handout.pdf · Adam Meadows Project Committee: Dr. Eamonn Keogh Dr. Doug Tolbert Roadmap •Problem

Slides by Eamonn Keogh Clustering. Organizing data into classes such that there is high intra-class similarity low inter-class similarity Finding the

Abdullah Mueen Eamonn Keogh University of California, Riverside

Exact Indexing of Dynamic Time Warping Dr Eamonn Keogh University of California – Riverside Computer Science & Engineering Department

Mining Time Series. (c) Eamonn Keogh, [email protected] 2 Why is Working With Time Series so Difficult? Part I 1 Hour of ECG data: w 1 Hour of ECG data: