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Eamonn J. Keogh: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Eamonn J. Keogh, Jessica Lin, Stefano Lonardi, Bill Yuan-chi Chiu
    We Have Seen the Future, and It Is Symbolic. [Citation Graph (0, 0)][DBLP]
    ACSW Frontiers, 2004, pp:83- [Conf]
  2. Ada Wai-Chee Fu, Oscar Tat-Wing Leung, Eamonn J. Keogh, Jessica Lin
    Finding Time Series Discords Based on Haar Transform. [Citation Graph (0, 0)][DBLP]
    ADMA, 2006, pp:31-41 [Conf]
  3. Jessica Lin, Eamonn J. Keogh, Ada Wai-Chee Fu, Helga Van Herle
    Approximations to Magic: Finding Unusual Medical Time Series. [Citation Graph (0, 0)][DBLP]
    CBMS, 2005, pp:329-334 [Conf]
  4. Li Wei, Nitin Kumar, Venkata Nishanth Lolla, Eamonn J. Keogh, Stefano Lonardi, Chotirat (Ann) Ratanamahatana, Helga Van Herle
    A Practical Tool for Visualizing and Data Mining Medical Time Series. [Citation Graph (0, 0)][DBLP]
    CBMS, 2005, pp:341-346 [Conf]
  5. Pablo Viana, Ann Gordon-Ross, Eamonn J. Keogh, Edna Barros, Frank Vahid
    Configurable cache subsetting for fast cache tuning. [Citation Graph (0, 0)][DBLP]
    DAC, 2006, pp:695-700 [Conf]
  6. Jessica Lin, Eamonn J. Keogh, Stefano Lonardi, Bill Yuan-chi Chiu
    A symbolic representation of time series, with implications for streaming algorithms. [Citation Graph (0, 0)][DBLP]
    DMKD, 2003, pp:2-11 [Conf]
  7. Jessica Lin, Eamonn J. Keogh, Wagner Truppel
    Clustering of streaming time series is meaningless. [Citation Graph (0, 0)][DBLP]
    DMKD, 2003, pp:56-65 [Conf]
  8. Dragomir Yankov, Dennis DeCoste, Eamonn J. Keogh
    Ensembles of Nearest Neighbor Forecasts. [Citation Graph (0, 0)][DBLP]
    ECML, 2006, pp:545-556 [Conf]
  9. Jessica Lin, Michail Vlachos, Eamonn J. Keogh, Dimitrios Gunopulos
    Iterative Incremental Clustering of Time Series. [Citation Graph (0, 0)][DBLP]
    EDBT, 2004, pp:106-122 [Conf]
  10. Eamonn J. Keogh, Harry Hochheiser, Ben Shneiderman
    An Augmented Visual Query Mechanism for Finding Patterns in Time Series Data. [Citation Graph (0, 0)][DBLP]
    FQAS, 2002, pp:240-250 [Conf]
  11. Chotirat (Ann) Ratanamahatana, Eamonn J. Keogh
    Multimedia Retrieval Using Time Series Representation and Relevance Feedback. [Citation Graph (0, 0)][DBLP]
    ICADL, 2005, pp:400-405 [Conf]
  12. Jessica Lin, Eamonn J. Keogh, Wagner Truppel
    (Not) Finding Rules in Time Series: A Surprising Result with Implications for Previous and Future Research. [Citation Graph (0, 0)][DBLP]
    IC-AI, 2003, pp:55-61 [Conf]
  13. Themistoklis Palpanas, Michail Vlachos, Eamonn J. Keogh, Dimitrios Gunopulos, Wagner Truppel
    Online Amnesic Approximation of Streaming Time Series. [Citation Graph (0, 0)][DBLP]
    ICDE, 2004, pp:338-349 [Conf]
  14. Eamonn J. Keogh, Selina Chu, David Hart, Michael J. Pazzani
    An Online Algorithm for Segmenting Time Series. [Citation Graph (0, 0)][DBLP]
    ICDM, 2001, pp:289-296 [Conf]
  15. Eamonn J. Keogh, Jessica Lin, Ada Wai-Chee Fu
    HOT SAX: Efficiently Finding the Most Unusual Time Series Subsequence. [Citation Graph (0, 0)][DBLP]
    ICDM, 2005, pp:226-233 [Conf]
  16. Eamonn J. Keogh, Jessica Lin, Wagner Truppel
    Clustering of Time Series Subsequences is Meaningless: Implications for Previous and Future Research. [Citation Graph (0, 0)][DBLP]
    ICDM, 2003, pp:115-122 [Conf]
  17. Longin Jan Latecki, Vasileios Megalooikonomou, Qiang Wang, Rolf Lakämper, Chotirat (Ann) Ratanamahatana, Eamonn J. Keogh
    Partial Elastic Matching of Time Series. [Citation Graph (0, 0)][DBLP]
    ICDM, 2005, pp:701-704 [Conf]
  18. Pranav Patel, Eamonn J. Keogh, Jessica Lin, Stefano Lonardi
    Mining Motifs in Massive Time Series Databases. [Citation Graph (0, 0)][DBLP]
    ICDM, 2002, pp:370-377 [Conf]
  19. Li Wei, Eamonn J. Keogh, Helga Van Herle, Agenor Mafra-Neto
    Atomic Wedgie: Efficient Query Filtering for Streaming Times Series. [Citation Graph (0, 0)][DBLP]
    ICDM, 2005, pp:490-497 [Conf]
  20. Li Wei, Eamonn J. Keogh, Xiaopeng Xi
    SAXually Explicit Images: Finding Unusual Shapes. [Citation Graph (0, 0)][DBLP]
    ICDM, 2006, pp:711-720 [Conf]
  21. Dragomir Yankov, Eamonn J. Keogh
    Manifold Clustering of Shapes. [Citation Graph (0, 0)][DBLP]
    ICDM, 2006, pp:1167-1171 [Conf]
  22. Eamonn J. Keogh, Li Wei, Xiaopeng Xi, Stefano Lonardi, Jin Shieh, Scott Sirowy
    Intelligent Icons: Integrating Lite-Weight Data Mining and Visualization into GUI Operating Systems. [Citation Graph (0, 0)][DBLP]
    ICDM, 2006, pp:912-916 [Conf]
  23. Ken Ueno, Xiaopeng Xi, Eamonn J. Keogh, Dah-Jye Lee
    Anytime Classification Using the Nearest Neighbor Algorithm with Applications to Stream Mining. [Citation Graph (0, 0)][DBLP]
    ICDM, 2006, pp:623-632 [Conf]
  24. Li Wei, John Handley, Nathaniel Martin, Tong Sun, Eamonn J. Keogh
    Clustering Workflow Requirements Using Compression Dissimilarity Measure. [Citation Graph (0, 0)][DBLP]
    ICDM Workshops, 2006, pp:50-54 [Conf]
  25. Xiaopeng Xi, Eamonn J. Keogh, Christian R. Shelton, Li Wei, Chotirat Ann Ratanamahatana
    Fast time series classification using numerosity reduction. [Citation Graph (0, 0)][DBLP]
    ICML, 2006, pp:1033-1040 [Conf]
  26. Eamonn J. Keogh
    Fast Similarity Search in the Presence of Longitudinal Scaling in Time Series Databases. [Citation Graph (0, 0)][DBLP]
    ICTAI, 1997, pp:578-584 [Conf]
  27. Dragomir Yankov, Eamonn J. Keogh, Stefano Lonardi, Ada Wai-Chee Fu
    Dot Plots for Time Series Analysis. [Citation Graph (0, 0)][DBLP]
    ICTAI, 2005, pp:159-168 [Conf]
  28. Jiyuan An, Hanxiong Chen, Kazutaka Furuse, Nobuo Ohbo, Eamonn J. Keogh
    Grid-Based Indexing for Large Time Series Databases. [Citation Graph (0, 0)][DBLP]
    IDEAL, 2003, pp:614-621 [Conf]
  29. Aris Anagnostopoulos, Michail Vlachos, Marios Hadjieleftheriou, Eamonn J. Keogh, Philip S. Yu
    Global distance-based segmentation of trajectories. [Citation Graph (0, 0)][DBLP]
    KDD, 2006, pp:34-43 [Conf]
  30. Bill Yuan-chi Chiu, Eamonn J. Keogh, Stefano Lonardi
    Probabilistic discovery of time series motifs. [Citation Graph (0, 0)][DBLP]
    KDD, 2003, pp:493-498 [Conf]
  31. Eamonn J. Keogh, Selina Chu, Michael J. Pazzani
    Ensemble-index: a new approach to indexing large databases. [Citation Graph (0, 0)][DBLP]
    KDD, 2001, pp:117-125 [Conf]
  32. Eamonn J. Keogh, Shruti Kasetty
    On the need for time series data mining benchmarks: a survey and empirical demonstration. [Citation Graph (0, 0)][DBLP]
    KDD, 2002, pp:102-111 [Conf]
  33. Eamonn J. Keogh, Stefano Lonardi, Bill Yuan-chi Chiu
    Finding surprising patterns in a time series database in linear time and space. [Citation Graph (0, 0)][DBLP]
    KDD, 2002, pp:550-556 [Conf]
  34. Eamonn J. Keogh, Stefano Lonardi, Chotirat (Ann) Ratanamahatana
    Towards parameter-free data mining. [Citation Graph (0, 0)][DBLP]
    KDD, 2004, pp:206-215 [Conf]
  35. Eamonn J. Keogh, Michael J. Pazzani
    Scaling up dynamic time warping for datamining applications. [Citation Graph (0, 0)][DBLP]
    KDD, 2000, pp:285-289 [Conf]
  36. Eamonn J. Keogh, Michael J. Pazzani
    An Enhanced Representation of Time Series Which Allows Fast and Accurate Classification, Clustering and Relevance Feedback. [Citation Graph (0, 0)][DBLP]
    KDD, 1998, pp:239-243 [Conf]
  37. Eamonn J. Keogh, Padhraic Smyth
    A Probabilistic Approach to Fast Pattern Matching in Time Series Databases. [Citation Graph (0, 0)][DBLP]
    KDD, 1997, pp:24-30 [Conf]
  38. Jessica Lin, Eamonn J. Keogh, Stefano Lonardi, Jeffrey P. Lankford, Donna M. Nystrom
    Visually mining and monitoring massive time series. [Citation Graph (0, 0)][DBLP]
    KDD, 2004, pp:460-469 [Conf]
  39. Michail Vlachos, Marios Hadjieleftheriou, Dimitrios Gunopulos, Eamonn J. Keogh
    Indexing multi-dimensional time-series with support for multiple distance measures. [Citation Graph (0, 0)][DBLP]
    KDD, 2003, pp:216-225 [Conf]
  40. Li Wei, Eamonn J. Keogh
    Semi-supervised time series classification. [Citation Graph (0, 0)][DBLP]
    KDD, 2006, pp:748-753 [Conf]
  41. Chotirat (Ann) Ratanamahatana, Eamonn J. Keogh
    Using Relevance Feedback to Learn Both the Distance Measure and the Query in Multimedia Databases. [Citation Graph (0, 0)][DBLP]
    KES (2), 2005, pp:16-23 [Conf]
  42. Petko Bakalov, Marios Hadjieleftheriou, Eamonn J. Keogh, Vassilis J. Tsotras
    Efficient trajectory joins using symbolic representations. [Citation Graph (0, 0)][DBLP]
    Mobile Data Management, 2005, pp:86-93 [Conf]
  43. Eamonn J. Keogh, Michael J. Pazzani
    A Simple Dimensionality Reduction Technique for Fast Similarity Search in Large Time Series Databases. [Citation Graph (0, 0)][DBLP]
    PAKDD, 2000, pp:122-133 [Conf]
  44. 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. [Citation Graph (0, 0)][DBLP]
    PAKDD, 2005, pp:333-342 [Conf]
  45. 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. [Citation Graph (0, 0)][DBLP]
    PAKDD, 2005, pp:771-777 [Conf]
  46. Eamonn J. Keogh
    Efficiently Finding Arbitrarily Scaled Patterns in Massive Time Series Databases. [Citation Graph (0, 0)][DBLP]
    PKDD, 2003, pp:253-265 [Conf]
  47. Eamonn J. Keogh
    Recent Advances in Mining Time Series Data. [Citation Graph (0, 0)][DBLP]
    PKDD, 2005, pp:6- [Conf]
  48. Eamonn J. Keogh, Michael J. Pazzani
    Scaling up Dynamic Time Warping to Massive Dataset. [Citation Graph (0, 0)][DBLP]
    PKDD, 1999, pp:1-11 [Conf]
  49. Longin Jan Latecki, Vasilis Megalooikonomou, Qiang Wang, Rolf Lakämper, Chotirat (Ann) Ratanamahatana, Eamonn J. Keogh
    Elastic Partial Matching of Time Series. [Citation Graph (0, 0)][DBLP]
    PKDD, 2005, pp:577-584 [Conf]
  50. Jessica Lin, Eamonn J. Keogh
    Group SAX: Extending the Notion of Contrast Sets to Time Series and Multimedia Data. [Citation Graph (0, 0)][DBLP]
    PKDD, 2006, pp:284-296 [Conf]
  51. Eamonn J. Keogh
    Indexing and Mining Time Series. [Citation Graph (0, 0)][DBLP]
    SBBD, 2002, pp:9- [Conf]
  52. Eamonn J. Keogh
    A Gentle Introduction to Machine Learning and Data Mining for the Database Community. [Citation Graph (0, 0)][DBLP]
    SBBD, 2003, pp:2- [Conf]
  53. Selina Chu, Eamonn J. Keogh, David Hart, Michael J. Pazzani
    Iterative Deepening Dynamic Time Warping for Time Series. [Citation Graph (0, 0)][DBLP]
    SDM, 2002, pp:- [Conf]
  54. Chotirat (Ann) Ratanamahatana, Eamonn J. Keogh
    Three Myths about Dynamic Time Warping Data Mining. [Citation Graph (0, 0)][DBLP]
    SDM, 2005, pp:- [Conf]
  55. Nitin Kumar, Venkata Nishanth Lolla, Eamonn J. Keogh, Stefano Lonardi, Chotirat (Ann) Ratanamahatana
    Time-series Bitmaps: a Practical Visualization Tool for Working with Large Time Series Databases. [Citation Graph (0, 0)][DBLP]
    SDM, 2005, pp:- [Conf]
  56. Chotirat (Ann) Ratanamahatana, Eamonn J. Keogh
    Making Time-Series Classification More Accurate Using Learned Constraints. [Citation Graph (0, 0)][DBLP]
    SDM, 2004, pp:- [Conf]
  57. Eamonn J. Keogh, Michael J. Pazzani
    Relevance Feedback Retrieval of Time Series Data. [Citation Graph (0, 0)][DBLP]
    SIGIR, 1999, pp:183-190 [Conf]
  58. Eamonn J. Keogh, Kaushik Chakrabarti, Sharad Mehrotra, Michael J. Pazzani
    Locally Adaptive Dimensionality Reduction for Indexing Large Time Series Databases. [Citation Graph (0, 0)][DBLP]
    SIGMOD Conference, 2001, pp:151-162 [Conf]
  59. Eamonn J. Keogh, Michael J. Pazzani
    An Indexing Scheme for Fast Similarity Search in Large Time Series Databases. [Citation Graph (0, 0)][DBLP]
    SSDBM, 1999, pp:56-67 [Conf]
  60. Li Wei, Nitin Kumar, Venkata Nishanth Lolla, Eamonn J. Keogh, Stefano Lonardi, Chotirat (Ann) Ratanamahatana
    Assumption-Free Anomaly Detection in Time Series. [Citation Graph (0, 0)][DBLP]
    SSDBM, 2005, pp:237-240 [Conf]
  61. Eamonn J. Keogh
    Visualization and Mining of Temporal Data. [Citation Graph (0, 0)][DBLP]
    IEEE Visualization, 2005, pp:126- [Conf]
  62. Ada Wai-Chee Fu, Eamonn J. Keogh, Leo Yung Hang Lau, Chotirat (Ann) Ratanamahatana
    Scaling and Time Warping in Time Series Querying. [Citation Graph (0, 0)][DBLP]
    VLDB, 2005, pp:649-660 [Conf]
  63. Eamonn J. Keogh
    Exact Indexing of Dynamic Time Warping. [Citation Graph (0, 0)][DBLP]
    VLDB, 2002, pp:406-417 [Conf]
  64. Eamonn J. Keogh
    A Decade of Progress in Indexing and Mining Large Time Series Databases. [Citation Graph (0, 0)][DBLP]
    VLDB, 2006, pp:1268- [Conf]
  65. Eamonn J. Keogh, Li Wei, Xiaopeng Xi, Sang-Hee Lee, Michail Vlachos
    LB_Keogh Supports Exact Indexing of Shapes under Rotation Invariance with Arbitrary Representations and Distance Measures. [Citation Graph (0, 0)][DBLP]
    VLDB, 2006, pp:882-893 [Conf]
  66. Jessica Lin, Eamonn J. Keogh, Stefano Lonardi, Jeffrey P. Lankford, Donna M. Nystrom
    VizTree: a Tool for Visually Mining and Monitoring Massive Time Series Databases. [Citation Graph (0, 0)][DBLP]
    VLDB, 2004, pp:1269-1272 [Conf]
  67. Eamonn J. Keogh, Themis Palpanas, Victor B. Zordan, Dimitrios Gunopulos, Marc Cardle
    Indexing Large Human-Motion Databases. [Citation Graph (0, 0)][DBLP]
    VLDB, 2004, pp:780-791 [Conf]
  68. Jiyuan An, Yi-Ping Phoebe Chen, Eamonn J. Keogh
    A Grid-Based Index Method for Time Warping Distance. [Citation Graph (0, 0)][DBLP]
    WAIM, 2004, pp:65-75 [Conf]
  69. Eamonn J. Keogh, Shruti Kasetty
    On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration. [Citation Graph (0, 0)][DBLP]
    Data Min. Knowl. Discov., 2003, v:7, n:4, pp:349-371 [Journal]
  70. Anthony J. Bagnall, Chotirat (Ann) Ratanamahatana, Eamonn J. Keogh, Stefano Lonardi, Gareth J. Janacek
    A Bit Level Representation for Time Series Data Mining with Shape Based Similarity. [Citation Graph (0, 0)][DBLP]
    Data Min. Knowl. Discov., 2006, v:13, n:1, pp:11-40 [Journal]
  71. Eamonn J. Keogh, Michael J. Pazzani
    Learning the Structure of Augmented Bayesian Classifiers. [Citation Graph (0, 0)][DBLP]
    International Journal on Artificial Intelligence Tools, 2002, v:11, n:4, pp:587-601 [Journal]
  72. Jessica Lin, Eamonn J. Keogh, Stefano Lonardi
    Visualizing and discovering non-trivial patterns in large time series databases. [Citation Graph (0, 0)][DBLP]
    Information Visualization, 2005, v:4, n:2, pp:61-82 [Journal]
  73. Li Wei, Eamonn J. Keogh, Xiaopeng Xi, Stefano Lonardi
    Integrating Lite-Weight but Ubiquitous Data Mining into GUI Operating Systems. [Citation Graph (0, 0)][DBLP]
    J. UCS, 2005, v:11, n:11, pp:1820-1834 [Journal]
  74. Eamonn J. Keogh, Kaushik Chakrabarti, Michael J. Pazzani, Sharad Mehrotra
    Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases. [Citation Graph (0, 0)][DBLP]
    Knowl. Inf. Syst., 2001, v:3, n:3, pp:263-286 [Journal]
  75. Eamonn J. Keogh, Jessica Lin
    Clustering of time-series subsequences is meaningless: implications for previous and future research. [Citation Graph (0, 0)][DBLP]
    Knowl. Inf. Syst., 2005, v:8, n:2, pp:154-177 [Journal]
  76. Eamonn J. Keogh, Chotirat (Ann) Ratanamahatana
    Exact indexing of dynamic time warping. [Citation Graph (0, 0)][DBLP]
    Knowl. Inf. Syst., 2005, v:7, n:3, pp:358-386 [Journal]
  77. Eamonn J. Keogh, Jessica Lin, Sang-Hee Lee, Helga Van Herle
    Finding the most unusual time series subsequence: algorithms and applications. [Citation Graph (0, 0)][DBLP]
    Knowl. Inf. Syst., 2007, v:11, n:1, pp:1-27 [Journal]
  78. Eamonn J. Keogh
    Guest Editorial. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2005, v:58, n:2-3, pp:103-105 [Journal]
  79. Kaushik Chakrabarti, Eamonn J. Keogh, Sharad Mehrotra, Michael J. Pazzani
    Locally adaptive dimensionality reduction for indexing large time series databases. [Citation Graph (0, 0)][DBLP]
    ACM Trans. Database Syst., 2002, v:27, n:2, pp:188-228 [Journal]
  80. Michail Vlachos, Marios Hadjieleftheriou, Dimitrios Gunopulos, Eamonn J. Keogh
    Indexing Multidimensional Time-Series. [Citation Graph (0, 0)][DBLP]
    VLDB J., 2006, v:15, n:1, pp:1-20 [Journal]
  81. Dragomir Yankov, Eamonn J. Keogh, Jose Medina, Bill Chiu, Victor B. Zordan
    Detecting time series motifs under uniform scaling. [Citation Graph (0, 0)][DBLP]
    KDD, 2007, pp:844-853 [Conf]
  82. Eamonn J. Keogh
    Data mining and information retrieval in time series/multimedia databases. [Citation Graph (0, 0)][DBLP]
    ACM Multimedia, 2006, pp:10- [Conf]
  83. Michail Vlachos, Bahar Taneri, Eamonn J. Keogh, Philip S. Yu
    Visual Exploration of Genomic Data. [Citation Graph (0, 0)][DBLP]
    PKDD, 2007, pp:613-620 [Conf]
  84. Yingyi Bu, Oscar Tat-Wing Leung, Ada Wai-Chee Fu, Eamonn J. Keogh, Jian Pei, Sam Meshkin
    WAT: Finding Top-K Discords in Time Series Database. [Citation Graph (0, 0)][DBLP]
    SDM, 2007, pp:- [Conf]
  85. Xiaopeng Xi, Eamonn J. Keogh, Li Wei, Agenor Mafra-Neto
    Finding Motifs in a Database of Shapes. [Citation Graph (0, 0)][DBLP]
    SDM, 2007, pp:- [Conf]
  86. Dragomir Yankov, Eamonn J. Keogh, Li Wei, Xiaopeng Xi, Wendy L. Hodges
    Fast Best-Match Shape Searching in Rotation Invariant Metric Spaces. [Citation Graph (0, 0)][DBLP]
    SDM, 2007, pp:- [Conf]
  87. Eamonn J. Keogh, Stefano Lonardi, Chotirat Ann Ratanamahatana, Li Wei, Sang-Hee Lee, John Handley
    Compression-based data mining of sequential data. [Citation Graph (0, 0)][DBLP]
    Data Min. Knowl. Discov., 2007, v:14, n:1, pp:99-129 [Journal]
  88. Jessica Lin, Eamonn J. Keogh, Li Wei, Stefano Lonardi
    Experiencing SAX: a novel symbolic representation of time series. [Citation Graph (0, 0)][DBLP]
    Data Min. Knowl. Discov., 2007, v:15, n:2, pp:107-144 [Journal]
  89. Li Wei, Eamonn J. Keogh, Helga Van Herle, Agenor Mafra-Neto, Russell J. Abbott
    Efficient query filtering for streaming time series with applications to semisupervised learning of time series classifiers. [Citation Graph (0, 0)][DBLP]
    Knowl. Inf. Syst., 2007, v:11, n:3, pp:313-344 [Journal]
  90. Ada Wai-Chee Fu, Eamonn J. Keogh, Leo Yung Hang Lau, Chotirat (Ann) Ratanamahatana, Raymond Chi-Wing Wong
    Scaling and time warping in time series querying. [Citation Graph (0, 0)][DBLP]
    VLDB J., 2008, v:0, n:, pp:- [Journal]

  91. TS2-tree - an efficient similarity based organization for trajectory data. [Citation Graph (, )][DBLP]


  92. Disk Aware Discord Discovery: Finding Unusual Time Series in Terabyte Sized Datasets. [Citation Graph (, )][DBLP]


  93. Locally Constrained Support Vector Clustering. [Citation Graph (, )][DBLP]


  94. Finding Time Series Motifs in Disk-Resident Data. [Citation Graph (, )][DBLP]


  95. Real-Time Classification of Streaming Sensor Data. [Citation Graph (, )][DBLP]


  96. Using CAPTCHAs to Index Cultural Artifacts. [Citation Graph (, )][DBLP]


  97. Augmenting Historical Manuscripts with Automatic Hyperlinks. [Citation Graph (, )][DBLP]


  98. Annotating historical archives of images. [Citation Graph (, )][DBLP]


  99. Finding centuries-old hyperlinks with a novel semi-supervised learning technique. [Citation Graph (, )][DBLP]


  100. Time series shapelets: a new primitive for data mining. [Citation Graph (, )][DBLP]


  101. iSAX: indexing and mining terabyte sized time series. [Citation Graph (, )][DBLP]


  102. Augmenting the generalized hough transform to enable the mining of petroglyphs. [Citation Graph (, )][DBLP]


  103. Online discovery and maintenance of time series motifs. [Citation Graph (, )][DBLP]


  104. The Asymmetric Approximate Anytime Join: A New Primitive with Applications to Data Mining. [Citation Graph (, )][DBLP]


  105. Autocannibalistic and Anyspace Indexing Algorithms with Application to Sensor Data Mining. [Citation Graph (, )][DBLP]


  106. Exact Discovery of Time Series Motifs. [Citation Graph (, )][DBLP]


  107. A Compression Based Distance Measure for Texture. [Citation Graph (, )][DBLP]


  108. Efficiently finding unusual shapes in large image databases. [Citation Graph (, )][DBLP]


  109. iSAX: disk-aware mining and indexing of massive time series datasets. [Citation Graph (, )][DBLP]


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