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Note: Links lead to the DBLP on the Web. Eamonn J. Keogh Jessica Lin , Michail Vlachos , Eamonn J. Keogh, Dimitrios Gunopulos , Jian-Wei Liu , Shou-Jian Yu , Jia-Jin Le : A MPAA-Based Iterative Clustering Algorithm Augmented by Nearest Neighbors Search for Time-Series Data Streams. PAKDD 2005 : 333-342 Chotirat (Ann) Ratanamahatana , Eamonn J. Keogh, Anthony J. Bagnall , Stefano Lonardi : A Novel Bit Level Time Series Representation with Implication of Similarity Search and Clustering. PAKDD 2005 : 771-777 Eamonn J. Keogh, Chotirat (Ann) Ratanamahatana : Exact indexing of dynamic time warping. Knowl. Inf. Syst. 7 (3): 358-386 (2005) Eamonn J. Keogh: Guest Editorial. Machine Learning 58 (2-3): 103-105 (2005) Eamonn J. Keogh, Jessica Lin , Stefano Lonardi , Bill Yuan-chi Chiu : We Have Seen the Future, and It Is Symbolic. ACSW Frontiers 2004 : 83 Jessica Lin , Michail Vlachos , Eamonn J. Keogh, Dimitrios Gunopulos : Iterative Incremental Clustering of Time Series. EDBT 2004 : 106-122 Themistoklis Palpanas , Michail Vlachos , Eamonn J. Keogh, Dimitrios Gunopulos , Wagner Truppel : Online Amnesic Approximation of Streaming Time Series. ICDE 2004 : 338-349 Eamonn J. Keogh, Stefano Lonardi , Chotirat (Ann) Ratanamahatana : Towards parameter-free data mining. KDD 2004 : 206-215 Jessica Lin , Eamonn J. Keogh, Stefano Lonardi , Jeffrey P. Lankford , Donna M. Nystrom : Visually mining and monitoring massive time series. KDD 2004 : 460-469 Chotirat (Ann) Ratanamahatana , Eamonn J. Keogh: Making Time-Series Classification More Accurate Using Learned Constraints. SDM 2004 Jessica Lin , Eamonn J. Keogh, Stefano Lonardi , Jeffrey P. Lankford , Daonna M. Nystrom : VizTree: a Tool for Visually Mining and Monitoring Massive Time Series Databases. VLDB 2004 : 1269-1272 Eamonn J. Keogh, Themis Palpanas , Victor B. Zordan , Dimitrios Gunopulos , Marc Cardle : Indexing Large Human-Motion Databases. VLDB 2004 : 780-791 Jiyuan An , Yi-Ping Phoebe Chen , Eamonn J. Keogh: A Grid-Based Index Method for Time Warping Distance. WAIM 2004 : 65-75 Jessica Lin , Eamonn J. Keogh, Stefano Lonardi , Bill Yuan-chi Chiu : A symbolic representation of time series, with implications for streaming algorithms. DMKD 2003 : 2-11 Jessica Lin , Eamonn J. Keogh, Wagner Truppel : Clustering of streaming time series is meaningless. DMKD 2003 : 56-65 Jessica Lin , Eamonn J. Keogh, Wagner Truppel : (Not) Finding Rules in Time Series: A Surprising Result with Implications for Previous and Future Research. IC-AI 2003 : 55-61 Eamonn J. Keogh, Jessica Lin , Wagner Truppel : Clustering of Time Series Subsequences is Meaningless: Implications for Previous and Future Research. ICDM 2003 : 115-122 Jiyuan An , Hanxiong Chen , Kazutaka Furuse , Nobuo Ohbo , Eamonn J. Keogh: Grid-Based Indexing for Large Time Series Databases. IDEAL 2003 : 614-621 Michail Vlachos , Marios Hadjieleftheriou , Dimitrios Gunopulos , Eamonn J. Keogh: Indexing multi-dimensional time-series with support for multiple distance measures. KDD 2003 : 216-225 Bill Yuan-chi Chiu , Eamonn J. Keogh, Stefano Lonardi : Probabilistic discovery of time series motifs. KDD 2003 : 493-498 Eamonn J. Keogh: Efficiently Finding Arbitrarily Scaled Patterns in Massive Time Series Databases. PKDD 2003 : 253-265 Eamonn J. Keogh: A Gentle Introduction to Machine Learning and Data Mining for the Database Community. SBBD 2003 : 2 Eamonn J. Keogh, Shruti Kasetty : On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration. Data Min. Knowl. Discov. 7 (4): 349-371 (2003) Eamonn J. Keogh, Harry Hochheiser , Ben Shneiderman : An Augmented Visual Query Mechanism for Finding Patterns in Time Series Data. FQAS 2002 : 240-250 Pranav Patel , Eamonn J. Keogh, Jessica Lin , Stefano Lonardi : Mining Motifs in Massive Time Series Databases. ICDM 2002 : 370-377 Eamonn J. Keogh, Shruti Kasetty : On the need for time series data mining benchmarks: a survey and empirical demonstration. KDD 2002 : 102-111 Eamonn J. Keogh, Stefano Lonardi , Bill Yuan-chi Chiu : Finding surprising patterns in a time series database in linear time and space. KDD 2002 : 550-556 Eamonn J. Keogh: Indexing and Mining Time Series. SBBD 2002 : 9 Selina Chu , Eamonn J. Keogh, David Hart , Michael J. Pazzani : Iterative Deepening Dynamic Time Warping for Time Series. SDM 2002 Eamonn J. Keogh: Exact Indexing of Dynamic Time Warping. VLDB 2002 : 406-417 Kaushik Chakrabarti , Eamonn J. Keogh, Sharad Mehrotra , Michael J. Pazzani : Locally adaptive dimensionality reduction for indexing large time series databases. ACM Trans. Database Syst. 27 (2): 188-228 (2002) Eamonn J. Keogh, Michael J. Pazzani : Learning the Structure of Augmented Bayesian Classifiers. International Journal on Artificial Intelligence Tools 11 (4): 587-601 (2002) Eamonn J. Keogh, Selina Chu , David Hart , Michael J. Pazzani : An Online Algorithm for Segmenting Time Series. ICDM 2001 : 289-296 Eamonn J. Keogh, Selina Chu , Michael J. Pazzani : Ensemble-index: a new approach to indexing large databases. KDD 2001 : 117-125 Eamonn J. Keogh, Kaushik Chakrabarti , Sharad Mehrotra , Michael J. Pazzani : Locally Adaptive Dimensionality Reduction for Indexing Large Time Series Databases. SIGMOD Conference 2001 Eamonn J. Keogh, Kaushik Chakrabarti , Michael J. Pazzani , Sharad Mehrotra : Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases. Knowl. Inf. Syst. 3 (3): 263-286 (2001) Eamonn J. Keogh, Michael J. Pazzani : Scaling up dynamic time warping for datamining applications. KDD 2000 : 285-289 Eamonn J. Keogh, Michael J. Pazzani : A Simple Dimensionality Reduction Technique for Fast Similarity Search in Large Time Series Databases. PAKDD 2000 : 122-133 Eamonn J. Keogh, Michael J. Pazzani : Scaling up Dynamic Time Warping to Massive Dataset. PKDD 1999 : 1-11 Eamonn J. Keogh, Michael J. Pazzani : Relevance Feedback Retrieval of Time Series Data. SIGIR 1999 : 183-190 Eamonn J. Keogh, Michael J. Pazzani : An Indexing Scheme for Fast Similarity Search in Large Time Series Databases. SSDBM 1999 : 56-67 Eamonn J. Keogh, Michael J. Pazzani : An Enhanced Representation of Time Series Which Allows Fast and Accurate Classification, Clustering and Relevance Feedback. KDD 1998 : 239-243 Eamonn J. Keogh: Fast Similarity Search in the Presence of Longitudinal Scaling in Time Series Databases. ICTAI 1997 : 578-584 Eamonn J. Keogh, Padhraic Smyth : A Probabilistic Approach to Fast Pattern Matching in Time Series Databases. KDD 1997 : 24-30 1 [ 27 ] [ 32 ] 2 [ 43 ] 3 [ 33 ] 4 [ 9 ] [ 10 ] [ 14 ] 5 [ 27 ] 6 [ 32 ] 7 [ 18 ] [ 25 ] [ 31 ] [ 40 ] 8 [ 11 ] [ 12 ] [ 16 ] 9 [ 27 ] 10 [ 26 ] [ 33 ] [ 38 ] [ 39 ] [ 44 ] 11 [ 26 ] 12 [ 12 ] [ 16 ] 13 [ 21 ] 14 [ 19 ] [ 22 ] 15 [ 34 ] [ 36 ] 16 [ 44 ] 17 [ 20 ] [ 28 ] [ 29 ] [ 30 ] [ 31 ] [ 34 ] [ 36 ] [ 39 ] [ 40 ] [ 44 ] 18 [ 44 ] 19 [ 18 ] [ 20 ] [ 25 ] [ 31 ] [ 34 ] [ 36 ] [ 37 ] [ 40 ] [ 43 ] 20 [ 9 ] [ 10 ] [ 14 ] 21 [ 34 ] 22 [ 36 ] 23 [ 27 ] 24 [ 33 ] 25 [ 38 ] 26 [ 20 ] 27 [ 3 ] [ 4 ] [ 5 ] [ 6 ] [ 7 ] [ 8 ] [ 9 ] [ 10 ] [ 11 ] [ 12 ] [ 13 ] [ 14 ] [ 16 ] 28 [ 35 ] [ 37 ] [ 42 ] [ 43 ] 29 [ 21 ] 30 [ 1 ] 31 [ 28 ] [ 29 ] [ 30 ] [ 38 ] 32 [ 26 ] [ 38 ] [ 39 ] [ 44 ] 33 [ 44 ] 34 [ 33 ]  ©2005 Association for Computing Machinery |
