Mining and similarity search in temporal databases

Zusammenfassung 1 1 Overview 5 1.

[1]  Hanan Samet,et al.  Foundations of multidimensional and metric data structures , 2006, Morgan Kaufmann series in data management systems.

[2]  Karl Aberer,et al.  Continuous query evaluation over distributed sensor networks , 2010, 2010 IEEE 26th International Conference on Data Engineering (ICDE 2010).

[3]  Thomas Seidl,et al.  CoDA: Interactive Cluster Based Concept Discovery , 2010, Proc. VLDB Endow..

[4]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.

[5]  Deborah F. Swayne,et al.  Grouping Multivariate Time Series : A Case Study , 2006 .

[6]  Hans-Peter Kriegel,et al.  Optimal multi-step k-nearest neighbor search , 1998, SIGMOD '98.

[7]  Elke Achtert,et al.  Evaluation of Clusterings -- Metrics and Visual Support , 2012, 2012 IEEE 28th International Conference on Data Engineering.

[8]  Beng Chin Ooi,et al.  iDistance: An adaptive B+-tree based indexing method for nearest neighbor search , 2005, TODS.

[9]  Tak-Chung Fu,et al.  A review on time series data mining , 2011, Eng. Appl. Artif. Intell..

[10]  Christos Faloutsos,et al.  Searching Multimedia Databases by Content , 1996, Advances in Database Systems.

[11]  Eamonn J. Keogh,et al.  Experimental comparison of representation methods and distance measures for time series data , 2010, Data Mining and Knowledge Discovery.

[12]  Thomas Seidl,et al.  Tracing Evolving Clusters by Subspace and Value Similarity , 2011, PAKDD.

[13]  Leonidas J. Guibas,et al.  The Earth Mover's Distance as a Metric for Image Retrieval , 2000, International Journal of Computer Vision.

[14]  Douglas H. Fisher,et al.  Knowledge Acquisition Via Incremental Conceptual Clustering , 1987, Machine Learning.

[15]  Eamonn J. Keogh,et al.  Time series shapelets: a new primitive for data mining , 2009, KDD.

[16]  Sheng-Fuu Lin,et al.  Time registration of two image sequences by dynamic time warping , 2004, IEEE International Conference on Networking, Sensing and Control, 2004.

[17]  Christos Faloutsos,et al.  FTW: fast similarity search under the time warping distance , 2005, PODS.

[18]  Matthew O. Ward,et al.  A Shared Execution Strategy for Multiple Pattern Mining Requests over Streaming Data , 2009, Proc. VLDB Endow..

[19]  Edmond H. C. Wu,et al.  Independent Component Analysis for Clustering Multivariate Time Series Data , 2005, ADMA.

[20]  Lei Chen,et al.  Robust and fast similarity search for moving object trajectories , 2005, SIGMOD '05.

[21]  L. Hubert,et al.  Measuring the Power of Hierarchical Cluster Analysis , 1975 .

[22]  Lin Zhang,et al.  Comparison of Cluster Representations from Partial Second- to Full Fourth-Order Cross Moments for Data Stream Clustering , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[23]  Paul Over,et al.  Evaluation campaigns and TRECVid , 2006, MIR '06.

[24]  Philip S. Yu,et al.  A Framework for Projected Clustering of High Dimensional Data Streams , 2004, VLDB.

[25]  Yifan Li,et al.  Clustering moving objects , 2004, KDD.

[26]  Padhraic Smyth,et al.  Trajectory clustering with mixtures of regression models , 1999, KDD '99.

[27]  L. Hubert,et al.  A general statistical framework for assessing categorical clustering in free recall. , 1976 .

[28]  Peter J. Rousseeuw,et al.  Finding Groups in Data: An Introduction to Cluster Analysis , 1990 .

[29]  Panos Kalnis,et al.  On Discovering Moving Clusters in Spatio-temporal Data , 2005, SSTD.

[30]  T. Warren Liao,et al.  Clustering of time series data - a survey , 2005, Pattern Recognit..

[31]  Ira Assent,et al.  External evaluation measures for subspace clustering , 2011, CIKM '11.

[32]  Ira Assent,et al.  Speeding Up Complex Video Copy Detection Queries , 2010, DASFAA.

[33]  Thomas Seidl,et al.  Subspace Clustering for Uncertain Data , 2010, SDM.

[34]  Hans-Peter Kriegel,et al.  The R*-tree: an efficient and robust access method for points and rectangles , 1990, SIGMOD '90.

[35]  Philip S. Yu,et al.  Fast algorithms for projected clustering , 1999, SIGMOD '99.

[36]  Hans-Peter Kriegel,et al.  Subspace Similarity Search: Efficient k-NN Queries in Arbitrary Subspaces , 2010, SSDBM.

[37]  Ping Chen,et al.  Using the fractal dimension to cluster datasets , 2000, KDD '00.

[38]  Ira Assent,et al.  Robust Adaptable Video Copy Detection , 2009, SSTD.

[39]  Hans-Peter Kriegel,et al.  Subspace and projected clustering: experimental evaluation and analysis , 2009, Knowledge and Information Systems.

[40]  Mohammed J. Zaki,et al.  TRICLUSTER: an effective algorithm for mining coherent clusters in 3D microarray data , 2005, SIGMOD '05.

[41]  Christos Faloutsos,et al.  Fast Nearest Neighbor Search in Medical Image Databases , 1996, VLDB.

[42]  Thomas Seidl,et al.  Efficient Index Support for View-Dependent Queries on CFD Data , 2007, SSTD.

[43]  Jonathan Goldstein,et al.  When Is ''Nearest Neighbor'' Meaningful? , 1999, ICDT.

[44]  Hans-Peter Kriegel,et al.  OPTICS: ordering points to identify the clustering structure , 1999, SIGMOD '99.

[45]  Elke Achtert,et al.  Finding Hierarchies of Subspace Clusters , 2006, PKDD.

[46]  Marios Hadjieleftheriou,et al.  R-Trees - A Dynamic Index Structure for Spatial Searching , 2008, ACM SIGSPATIAL International Workshop on Advances in Geographic Information Systems.

[47]  Masatoshi Yoshikawa,et al.  The A-tree: An Index Structure for High-Dimensional Spaces Using Relative Approximation , 2000, VLDB.

[48]  Christian Sohler,et al.  StreamKM++: A clustering algorithm for data streams , 2010, JEAL.

[49]  Edward R. Dougherty,et al.  Model-based evaluation of clustering validation measures , 2007, Pattern Recognit..

[50]  Jessica Lin,et al.  Finding Motifs in Time Series , 2002, KDD 2002.

[51]  Thomas Seidl,et al.  MOA: Massive Online Analysis, a Framework for Stream Classification and Clustering , 2010, WAPA.

[52]  Dimitrios Gunopulos,et al.  Discovering similar multidimensional trajectories , 2002, Proceedings 18th International Conference on Data Engineering.

[53]  Michael E. Houle,et al.  Dimensional Testing for Multi-step Similarity Search , 2012, 2012 IEEE 12th International Conference on Data Mining.

[54]  Hans-Peter Kriegel,et al.  Can Shared-Neighbor Distances Defeat the Curse of Dimensionality? , 2010, SSDBM.

[55]  Thomas Seidl,et al.  Stream Data Mining Using the MOA Framework , 2012, DASFAA.

[56]  A. Longhurst.  Ecological Geography of the Sea , 1998 .

[57]  Clement T. Yu,et al.  Haar Wavelets for Efficient Similarity Search of Time-Series: With and Without Time Warping , 2003, IEEE Trans. Knowl. Data Eng..

[58]  Thomas Seidl,et al.  Nesting the earth mover's distance for effective cluster tracing , 2013, SSDBM.

[59]  Thomas Seidl,et al.  Benchmarking Stream Clustering Algorithms within the MOA Framework , 2010 .

[60]  Huan Liu,et al.  Subspace clustering for high dimensional data: a review , 2004, SKDD.

[61]  Kotagiri Ramamohanarao,et al.  iVAT and aVAT: Enhanced Visual Analysis for Cluster Tendency Assessment , 2010, PAKDD.

[62]  W. Hargrove,et al.  Using Clustered Climate Regimes to Analyze and Compare Predictions from Fully Coupled General Circulation Models , 2005 .

[63]  Hui Xiong,et al.  Adapting the right measures for K-means clustering , 2009, KDD.

[64]  Christos Faloutsos,et al.  Beyond uniformity and independence: analysis of R-trees using the concept of fractal dimension , 1994, PODS.

[65]  Stan Salvador,et al.  FastDTW: Toward Accurate Dynamic Time Warping in Linear Time and Space , 2004 .

[66]  Dimitrios Gunopulos,et al.  Indexing multi-dimensional time-series with support for multiple distance measures , 2003, KDD '03.

[67]  George M. Church,et al.  Aligning gene expression time series with time warping algorithms , 2001, Bioinform..

[68]  Eamonn J. Keogh,et al.  Iterative Deepening Dynamic Time Warping for Time Series , 2002, SDM.

[69]  Eamonn J. Keogh,et al.  Locally adaptive dimensionality reduction for indexing large time series databases , 2001, SIGMOD '01.

[70]  Thomas Seidl,et al.  MCExplorer: Interactive Exploration of Multiple (Subspace) Clustering Solutions , 2010, 2010 IEEE International Conference on Data Mining Workshops.

[71]  Eamonn J. Keogh,et al.  Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases , 2001, Knowledge and Information Systems.

[72]  S. vanDongen.  Performance criteria for graph clustering and Markov cluster experiments , 2000 .

[73]  Ira Assent,et al.  Approximation Techniques for Indexing the Earth Mover’s Distance in Multimedia Databases , 2006, 22nd International Conference on Data Engineering (ICDE'06).

[74]  Charles E. Clark,et al.  Monte Carlo , 2006 .

[75]  Qi Tian,et al.  Content-based video identification: a survey , 2003, International Conference on Information Technology: Research and Education, 2003. Proceedings. ITRE2003..

[76]  Thomas Seidl,et al.  MOA: A Real-Time Analytics Open Source Framework , 2011, ECML/PKDD.

[77]  G. W. Hughes,et al.  Minimum Prediction Residual Principle Applied to Speech Recognition , 1975 .

[78]  Eamonn J. Keogh,et al.  Making Time-Series Classification More Accurate Using Learned Constraints , 2004, SDM.

[79]  Thomas Seidl,et al.  A Subspace Clustering Extension for the KNIME Data Mining Framework , 2012, 2012 IEEE 12th International Conference on Data Mining Workshops.

[80]  Sudipto Guha,et al.  Streaming-data algorithms for high-quality clustering , 2002, Proceedings 18th International Conference on Data Engineering.

[81]  Tim Oates,et al.  Identifying distinctive subsequences in multivariate time series by clustering , 1999, KDD '99.

[82]  Ira Assent,et al.  Relevant Subspace Clustering: Mining the Most Interesting Non-redundant Concepts in High Dimensional Data , 2009, 2009 Ninth IEEE International Conference on Data Mining.

[83]  G. W. Milligan,et al.  A monte carlo study of thirty internal criterion measures for cluster analysis , 1981 .

[84]  Charu C. Aggarwal,et al.  On change diagnosis in evolving data streams , 2005, IEEE Transactions on Knowledge and Data Engineering.

[85]  Edward Y. Chang,et al.  Adaptive non-linear clustering in data streams , 2006, CIKM '06.

[86]  Hans-Peter Kriegel,et al.  Clustering high-dimensional data: A survey on subspace clustering, pattern-based clustering, and correlation clustering , 2009, TKDD.

[87]  Liang Wang,et al.  Structure-Based Statistical Features and Multivariate Time Series Clustering , 2007, Seventh IEEE International Conference on Data Mining (ICDM 2007).

[88]  Ira Assent,et al.  Adaptable Distance Functions for Similarity-based Multimedia Retrieval , 2006, Datenbank-Spektrum.

[89]  G. W. Milligan,et al.  An examination of the effect of six types of error perturbation on fifteen clustering algorithms , 1980 .

[90]  Thomas Seidl,et al.  An effective evaluation measure for clustering on evolving data streams , 2011, KDD.

[91]  Ian F. C. Smith,et al.  A comprehensive validity index for clustering , 2008, Intell. Data Anal..

[92]  Jennifer Widom,et al.  Continuous queries over data streams , 2001, SGMD.

[93]  Aoying Zhou,et al.  Density-Based Clustering over an Evolving Data Stream with Noise , 2006, SDM.

[94]  George Karypis,et al.  Empirical and Theoretical Comparisons of Selected Criterion Functions for Document Clustering , 2004, Machine Learning.

[95]  Michalis Vazirgiannis,et al.  c ○ 2001 Kluwer Academic Publishers. Manufactured in The Netherlands. On Clustering Validation Techniques , 2022 .

[96]  Philip S. Yu,et al.  A Framework for Clustering Evolving Data Streams , 2003, VLDB.

[97]  Geoff Holmes,et al.  New ensemble methods for evolving data streams , 2009, KDD.

[98]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[99]  Li Chen,et al.  Video copy detection: a comparative study , 2007, CIVR '07.

[100]  John A. Hartigan,et al.  Clustering Algorithms , 1975 .

[101]  Leonidas J. Guibas,et al.  Predictive QoS routing to mobile sinks in wireless sensor networks , 2009, 2009 International Conference on Information Processing in Sensor Networks.

[102]  Lei Chen,et al.  On The Marriage of Lp-norms and Edit Distance , 2004, VLDB.

[103]  Jian Pei,et al.  Mining coherent gene clusters from gene-sample-time microarray data , 2004, KDD.

[104]  Beng Chin Ooi,et al.  Indexing the Distance: An Efficient Method to KNN Processing , 2001, VLDB.

[105]  Thomas Seidl,et al.  An Evaluation Framework for Temporal Subspace Clustering Approaches , 2013, 2013 IEEE 13th International Conference on Data Mining Workshops.

[106]  Hans-Peter Kriegel,et al.  A generic framework for efficient subspace clustering of high-dimensional data , 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).

[107]  Hans-Peter Kriegel,et al.  Density-based Projected Clustering over High Dimensional Data Streams , 2012, SDM.

[108]  Marina Meila,et al.  Comparing subspace clusterings , 2006, IEEE Transactions on Knowledge and Data Engineering.

[109]  Thomas Seidl,et al.  Detecting Climate Change in Multivariate Time Series Data by Novel Clustering and Cluster Tracing Techniques , 2010, 2010 IEEE International Conference on Data Mining Workshops.

[110]  Myra Spiliopoulou,et al.  On exploiting the power of time in data mining , 2008, SKDD.

[111]  Eamonn J. Keogh,et al.  Three Myths about Dynamic Time Warping Data Mining , 2005, SDM.

[112]  Thomas Seidl,et al.  An extension of the PMML standard to subspace clustering models , 2011, PMML '11.

[113]  Ruud M. Bolle,et al.  Comparison of sequence matching techniques for video copy detection , 2001, IS&T/SPIE Electronic Imaging.

[114]  Ka Yee Yeung,et al.  Validating clustering for gene expression data , 2001, Bioinform..

[115]  Christos Faloutsos,et al.  Efficient Similarity Search In Sequence Databases , 1993, FODO.

[116]  Dennis Shasha,et al.  Warping indexes with envelope transforms for query by humming , 2003, SIGMOD '03.

[117]  Julia Hirschberg,et al.  V-Measure: A Conditional Entropy-Based External Cluster Evaluation Measure , 2007, EMNLP.

[118]  Hans-Peter Kriegel,et al.  The X-tree : An Index Structure for High-Dimensional Data , 2001, VLDB.

[119]  Ira Assent,et al.  Self-Adaptive Anytime Stream Clustering , 2009, 2009 Ninth IEEE International Conference on Data Mining.

[120]  S. Chiba,et al.  Dynamic programming algorithm optimization for spoken word recognition , 1978 .

[121]  Wesley W. Chu,et al.  An index-based approach for similarity search supporting time warping in large sequence databases , 2001, Proceedings 17th International Conference on Data Engineering.

[122]  Donald J. Berndt,et al.  Using Dynamic Time Warping to Find Patterns in Time Series , 1994, KDD Workshop.

[123]  Mi Zhou,et al.  Boundary-Based Lower-Bound Functions for Dynamic Time Warping and Their Indexing , 2007, ICDE.

[124]  I. Jolliffe.  Principal Component Analysis , 2002 .

[125]  Christos Faloutsos,et al.  Efficient retrieval of similar time sequences under time warping , 1998, Proceedings 14th International Conference on Data Engineering.

[126]  Anthony K. H. Tung,et al.  SpADe: On Shape-based Pattern Detection in Streaming Time Series , 2007, 2007 IEEE 23rd International Conference on Data Engineering.

[127]  Zhen Hu,et al.  Algorithm for Discovering Low-Variance 3-Clusters from Real-Valued Datasets , 2010, 2010 IEEE International Conference on Data Mining.

[128]  Hans-Peter Kriegel,et al.  Efficiently supporting multiple similarity queries for mining in metric databases , 2000, Proceedings of 16th International Conference on Data Engineering (Cat. No.00CB37073).

[129]  Christos Faloutsos,et al.  The TV-tree: An index structure for high-dimensional data , 1994, The VLDB Journal.

[130]  William M. Rand,et al.  Objective Criteria for the Evaluation of Clustering Methods , 1971 .

[131]  Thomas Seidl,et al.  Subspace clustering for indexing high dimensional data: a main memory index based on local reductions and individual multi-representations , 2011, EDBT/ICDT '11.

[132]  Thomas Seidl,et al.  Effective and Robust Mining of Temporal Subspace Clusters , 2012, 2012 IEEE 12th International Conference on Data Mining.

[133]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[134]  Pavel Zezula,et al.  M-tree: An Efficient Access Method for Similarity Search in Metric Spaces , 1997, VLDB.

[135]  Thorsten Meinl,et al.  KNIME: The Konstanz Information Miner , 2007, GfKl.

[136]  Hui Ding,et al.  Querying and mining of time series data: experimental comparison of representations and distance measures , 2008, Proc. VLDB Endow..

[137]  Clu-istos Foutsos,et al.  Fast subsequence matching in time-series databases , 1994, SIGMOD '94.

[138]  Li Tu,et al.  Density-based clustering for real-time stream data , 2007, KDD '07.

[139]  Philip S. Yu,et al.  Finding generalized projected clusters in high dimensional spaces , 2000, SIGMOD '00.

[140]  F. Rohlf.  Methods of Comparing Classifications , 1974 .

[141]  Aoying Zhou,et al.  An adaptive and dynamic dimensionality reduction method for high-dimensional indexing , 2007, The VLDB Journal.

[142]  Saeed Ayat,et al.  New Approach in Data Stream Association Rule Mining Based on Graph Structure , 2010, ICDM.

[143]  Bernd Gärtner,et al.  Fast and Robust Smallest Enclosing Balls , 1999, ESA.

[144]  Ira Assent,et al.  Evaluating Clustering in Subspace Projections of High Dimensional Data , 2009, Proc. VLDB Endow..

[145]  Eamonn J. Keogh,et al.  Exact indexing of dynamic time warping , 2002, Knowledge and Information Systems.

[146]  Joachim M. Buhmann,et al.  Stability-Based Model Order Selection in Clustering with Applications to Gene Expression Data , 2002, ICANN.

[147]  Dimitrios Gunopulos,et al.  Automatic subspace clustering of high dimensional data for data mining applications , 1998, SIGMOD '98.

[148]  Piotr Indyk,et al.  Similarity Search in High Dimensions via Hashing , 1999, VLDB.

[149]  R. Manmatha,et al.  Lower-Bounding of Dynamic Time Warping Distances for Multivariate Time Series , 2003 .

[150]  Raj Bhatnagar,et al.  An effective algorithm for mining 3-clusters in vertically partitioned data , 2008, CIKM '08.

[151]  Ira Assent,et al.  High-Dimensional Indexing for Multimedia Features , 2009, BTW.

[152]  Ira Assent,et al.  The TS-tree: efficient time series search and retrieval , 2008, EDBT '08.

[153]  Eamonn J. Keogh,et al.  LB_Keogh supports exact indexing of shapes under rotation invariance with arbitrary representations and distance measures , 2006, VLDB.

[154]  Marina Meila,et al.  Comparing clusterings: an axiomatic view , 2005, ICML.

[155]  Thomas G. Dietterich.  What is machine learning? , 2020, Archives of Disease in Childhood.

[156]  Anil K. Jain,et al.  Algorithms for Clustering Data , 1988 .

[157]  Hans-Jörg Schek,et al.  A Quantitative Analysis and Performance Study for Similarity-Search Methods in High-Dimensional Spaces , 1998, VLDB.

[158]  D. A. Siegel,et al.  The North Atlantic Spring Phytoplankton Bloom and Sverdrup's Critical Depth Hypothesis , 2002, Science.

[159]  Thomas Seidl,et al.  Mining of Temporal Coherent Subspace Clusters in Multivariate Time Series Databases , 2012, PAKDD.

[160]  Ali S. Hadi,et al.  Finding Groups in Data: An Introduction to Chster Analysis , 1991 .

[161]  Christian Böhm,et al.  Density connected clustering with local subspace preferences , 2004, Fourth IEEE International Conference on Data Mining (ICDM'04).

[162]  Ira Assent,et al.  Pattern detector: fast detection of suspicious stream patterns for immediate reaction , 2010, EDBT '10.

[163]  Eamonn J. Keogh,et al.  Atomic wedgie: efficient query filtering for streaming time series , 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).

[164]  Myra Spiliopoulou,et al.  MONIC: modeling and monitoring cluster transitions , 2006, KDD '06.

[165]  Thomas Seidl,et al.  Clustering Performance on Evolving Data Streams: Assessing Algorithms and Evaluation Measures within MOA , 2010, 2010 IEEE International Conference on Data Mining Workshops.

[166]  Hui Xiong,et al.  Understanding of Internal Clustering Validation Measures , 2010, 2010 IEEE International Conference on Data Mining.

[167]  Timos K. Sellis,et al.  Multiple-query optimization , 1988, TODS.

[168]  Meinard Müller,et al.  Information retrieval for music and motion , 2007 .

[169]  Shengrui Wang,et al.  An objective approach to cluster validation , 2006, Pattern Recognit. Lett..

[170]  Beng Chin Ooi,et al.  Continuous Clustering of Moving Objects , 2007, IEEE Transactions on Knowledge and Data Engineering.

[171]  Man Lung Yiu,et al.  Frequent-pattern based iterative projected clustering , 2003, Third IEEE International Conference on Data Mining.

[172]  Kelvin Sim,et al.  Discovering Correlated Subspace Clusters in 3D Continuous-Valued Data , 2010, 2010 IEEE International Conference on Data Mining.

[173]  Sharad Mehrotra,et al.  Local Dimensionality Reduction: A New Approach to Indexing High Dimensional Spaces , 2000, VLDB.

[174]  Kanad Ghose,et al.  Detecting and Tracking Spatio-temporal Clusters with Adaptive History Filtering , 2008, 2008 IEEE International Conference on Data Mining Workshops.

[175]  L. Hubert,et al.  Comparing partitions , 1985 .

[176]  Philip Chan,et al.  Toward accurate dynamic time warping in linear time and space , 2007, Intell. Data Anal..

[177]  Emmanuel Müller,et al.  Detection of orthogonal concepts in subspaces of high dimensional data , 2009, CIKM.

[178]  Pierre Hansen,et al.  NP-hardness of Euclidean sum-of-squares clustering , 2008, Machine Learning.

[179]  J. Dunn.  Well-Separated Clusters and Optimal Fuzzy Partitions , 1974 .

[180]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[181]  Ira Assent,et al.  Efficient Processing of Multiple DTW Queries in Time Series Databases , 2011, SSDBM.

[182]  Wee Hyong Tok,et al.  Efficient and Adaptive Processing of Multiple Continuous Queries , 2002, EDBT.

[183]  Michalis Vazirgiannis,et al.  A density-based cluster validity approach using multi-representatives , 2008, Pattern Recognit. Lett..

[184]  Ira Assent,et al.  Anticipatory DTW for Efficient Similarity Search in Time Series Databases , 2009, Proc. VLDB Endow..

[185]  C. Mallows,et al.  A Method for Comparing Two Hierarchical Clusterings , 1983 .

[186]  T. M. Murali,et al.  A Monte Carlo algorithm for fast projective clustering , 2002, SIGMOD '02.

[187]  Dimitrios Gunopulos,et al.  Approximate embedding-based subsequence matching of time series , 2008, SIGMOD Conference.

[188]  Hans-Peter Kriegel,et al.  Density-Connected Subspace Clustering for High-Dimensional Data , 2004, SDM.

[189]  Panos Kalnis,et al.  Quality and efficiency in high dimensional nearest neighbor search , 2009, SIGMOD Conference.