A new shifting grid clustering algorithm

A new density- and grid-based type clustering algorithm using the concept of shifting grid is proposed. The proposed algorithm is a non-parametric type, which does not require users inputting parameters. It divides each dimension of the data space into certain intervals to form a grid structure in t...

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Vydáno v:Pattern recognition Ročník 37; číslo 3; s. 503 - 514
Hlavní autoři: W.M. Ma, Eden, Chow, Tommy W.S.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.03.2004
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ISSN:0031-3203, 1873-5142
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Abstract A new density- and grid-based type clustering algorithm using the concept of shifting grid is proposed. The proposed algorithm is a non-parametric type, which does not require users inputting parameters. It divides each dimension of the data space into certain intervals to form a grid structure in the data space. Based on the concept of sliding window, shifting of the whole grid structure is introduced to obtain a more descriptive density profile. As a result, we are able to enhance the accuracy of the results. Compared with many conventional algorithms, this algorithm is computational efficient because it clusters data in a way of cell rather than in points.
AbstractList A new density- and grid-based type clustering algorithm using the concept of shifting grid is proposed. The proposed algorithm is a non-parametric type, which does not require users inputting parameters. It divides each dimension of the data space into certain intervals to form a grid structure in the data space. Based on the concept of sliding window, shifting of the whole grid structure is introduced to obtain a more descriptive density profile. As a result, we are able to enhance the accuracy of the results. Compared with many conventional algorithms, this algorithm is computational efficient because it clusters data in a way of cell rather than in points.
Author W.M. Ma, Eden
Chow, Tommy W.S.
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  email: eetchow@cityu.edu.hk
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Cites_doi 10.1016/S0031-3203(00)00002-9
10.1016/S0893-6080(02)00075-8
10.1109/ICDE.1999.754967
10.1080/01969727308546046
10.1145/304182.304187
10.1109/ICDE.1999.754914
10.1109/34.824819
10.1145/331499.331504
10.1109/2.781637
10.1006/jcom.2001.0633
10.1145/276304.276312
10.1023/A:1009769707641
10.1145/235968.233324
10.1007/s007780050009
10.1145/276304.276314
10.1109/ICII.2001.983048
10.1109/ICPR.1996.546732
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Shifting grid
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References M. Ester, H.-P. Kriegel, J. Sander, X. Xu, A density-based algorithm for discovering clusters in large spatial data bases with noise, in: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, 1996.
Jain, Murty, Flynn (BIB4) 1999; 31
Yin (BIB26) 2002; 15
R. Agrawal, J. Gehrke, D. Gunopulos, P. Raghavan, Automatic subspace clustering of high dimensional data for data mining applications, Proceedings of the ACM SIGMOD International Conference on Management of data, 1998, pp. 94–105.
Jain, Duin, Mao (BIB2) 2000; 22
E. Schikuta, Grid-clustering: an efficient hierarchical clustering method for very large data sets, Proceedings of ICPR, 1996, pp. 101–105.
J.B. MacQueen, Some methods for classification and analysis of multivariate observations, Proceedings of the 5th Berkeley Symposium on Mathematics Statistics and Probability, 1967, pp. 281–297.
A. Hinneburg, D.A. Keim, An Efficient Approach to Clustering in Large Multimedia Data bases with Noise, American Association for Artificial Intelligence, Proceedings of the 1998 International Conference on Knowledge Discovery and Data Mining (KDD’98), pp. 58–65.
S. Guha, R. Rastogi, K. Shim, CURE: an efficient clustering algorithm for large data bases, Proceedings of the 1998 ACM SIGMOD International Conference on Management of Data, Vol. 27, No. 2, 1998, pp. 73–84.
M. Ankerst, M.M. Breunig, H.-P. Kriegel, J. Sander, OPTICS: ordering points to identify the clustering structure, in: Proceeding of International Conference on Management of Data, 1999, pp. 49–60.
Z. Huang, A fast clustering algorithm to cluster very large categorical data sets in data mining, in: SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery, 1997.
Y.C. Zhao, J. Song, GDILC: a grid-based density-isoline clustering algorithm, Proceedings of International Conferences on Info-tech and Info-net, Vol. 3, 2001, pp. 140–145.
T. Zhang, R. Ramakrishnan, M. Livny, BIRCH: an efficient data clustering method for very large data bases, Proceedings of the ACM SIGMOD International Conference on Management of Data, Vol. 25, No. 2 1996, pp. 103–114.
P. Berkhin, Survey of Clustering Data Mining Techniques
Vrahatics, Boutsinas, Alevizos, Pavlides (BIB15) 2002; 18
Han, Kamber (BIB1) 2001
Huang (BIB13) 1998; 2
Karypis, Han, Kumar (BIB8) 1999; 32
.
Everitt, Landau, Leese (BIB3) 2001
J.C. Dunn, A fuzzy relative of the ISODATA process and its use detecting compact well-separated clusters, Journal of Cybernetics 3 (1974) 32–57.
Wong, Chen, Su (BIB14) 2001; 34
S. Guha, R. Rastogi, K. Shim, ROCK: a robust clustering algorithm for categorical attributes, Proceedings of 15th International Conference on Data Engineering, 1999, pp. 512–521.
W. Wang, J. Yang, R. Muntz, STING+: an approach to active spatial data mining, Proceedings of 15th International Conference on Data Engineering, 1999, pp. 116–125.
W. Wang, J. Yang, R. Muntz, STING: a statistical information grid approach to spatial data mining, Proceedings of the International Conference on Very Large Data Bases, 1997, pp. 186–195.
Sheikholeslami, Chatterjee, Zhang (BIB21) 2000; 8
10.1016/j.patcog.2003.08.014_BIB7
10.1016/j.patcog.2003.08.014_BIB20
10.1016/j.patcog.2003.08.014_BIB6
Karypis (10.1016/j.patcog.2003.08.014_BIB8) 1999; 32
10.1016/j.patcog.2003.08.014_BIB5
10.1016/j.patcog.2003.08.014_BIB9
Everitt (10.1016/j.patcog.2003.08.014_BIB3) 2001
Yin (10.1016/j.patcog.2003.08.014_BIB26) 2002; 15
10.1016/j.patcog.2003.08.014_BIB19
10.1016/j.patcog.2003.08.014_BIB11
10.1016/j.patcog.2003.08.014_BIB12
Sheikholeslami (10.1016/j.patcog.2003.08.014_BIB21) 2000; 8
10.1016/j.patcog.2003.08.014_BIB17
10.1016/j.patcog.2003.08.014_BIB18
10.1016/j.patcog.2003.08.014_BIB16
Jain (10.1016/j.patcog.2003.08.014_BIB4) 1999; 31
10.1016/j.patcog.2003.08.014_BIB10
Han (10.1016/j.patcog.2003.08.014_BIB1) 2001
Huang (10.1016/j.patcog.2003.08.014_BIB13) 1998; 2
Wong (10.1016/j.patcog.2003.08.014_BIB14) 2001; 34
Vrahatics (10.1016/j.patcog.2003.08.014_BIB15) 2002; 18
10.1016/j.patcog.2003.08.014_BIB24
10.1016/j.patcog.2003.08.014_BIB25
10.1016/j.patcog.2003.08.014_BIB22
10.1016/j.patcog.2003.08.014_BIB23
Jain (10.1016/j.patcog.2003.08.014_BIB2) 2000; 22
References_xml – year: 2001
  ident: BIB3
  publication-title: Cluster Analysis
– reference: T. Zhang, R. Ramakrishnan, M. Livny, BIRCH: an efficient data clustering method for very large data bases, Proceedings of the ACM SIGMOD International Conference on Management of Data, Vol. 25, No. 2 1996, pp. 103–114.
– volume: 15
  start-page: 1005
  year: 2002
  end-page: 1016
  ident: BIB26
  article-title: Data visualisation and manifold mappling using the ViSOM
  publication-title: Neural Networks
– volume: 34
  start-page: 425
  year: 2001
  end-page: 442
  ident: BIB14
  article-title: A novel algorithm for data clustering
  publication-title: Pattern Recognition
– reference: Z. Huang, A fast clustering algorithm to cluster very large categorical data sets in data mining, in: SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery, 1997.
– reference: R. Agrawal, J. Gehrke, D. Gunopulos, P. Raghavan, Automatic subspace clustering of high dimensional data for data mining applications, Proceedings of the ACM SIGMOD International Conference on Management of data, 1998, pp. 94–105.
– reference: J.B. MacQueen, Some methods for classification and analysis of multivariate observations, Proceedings of the 5th Berkeley Symposium on Mathematics Statistics and Probability, 1967, pp. 281–297.
– reference: W. Wang, J. Yang, R. Muntz, STING+: an approach to active spatial data mining, Proceedings of 15th International Conference on Data Engineering, 1999, pp. 116–125.
– year: 2001
  ident: BIB1
  publication-title: Data Mining Concepts and Techniques
– volume: 2
  start-page: 283
  year: 1998
  end-page: 304
  ident: BIB13
  article-title: Extensions to the k-means algorithm for clustering large data sets with categorical values
  publication-title: Data Mining Knowledge Discovery
– volume: 8
  start-page: 289
  year: 2000
  end-page: 304
  ident: BIB21
  article-title: WaveCluster
  publication-title: VLDB J.
– reference: E. Schikuta, Grid-clustering: an efficient hierarchical clustering method for very large data sets, Proceedings of ICPR, 1996, pp. 101–105.
– reference: Y.C. Zhao, J. Song, GDILC: a grid-based density-isoline clustering algorithm, Proceedings of International Conferences on Info-tech and Info-net, Vol. 3, 2001, pp. 140–145.
– reference: P. Berkhin, Survey of Clustering Data Mining Techniques,
– reference: M. Ester, H.-P. Kriegel, J. Sander, X. Xu, A density-based algorithm for discovering clusters in large spatial data bases with noise, in: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, 1996.
– reference: S. Guha, R. Rastogi, K. Shim, ROCK: a robust clustering algorithm for categorical attributes, Proceedings of 15th International Conference on Data Engineering, 1999, pp. 512–521.
– reference: M. Ankerst, M.M. Breunig, H.-P. Kriegel, J. Sander, OPTICS: ordering points to identify the clustering structure, in: Proceeding of International Conference on Management of Data, 1999, pp. 49–60.
– reference: W. Wang, J. Yang, R. Muntz, STING: a statistical information grid approach to spatial data mining, Proceedings of the International Conference on Very Large Data Bases, 1997, pp. 186–195.
– reference: J.C. Dunn, A fuzzy relative of the ISODATA process and its use detecting compact well-separated clusters, Journal of Cybernetics 3 (1974) 32–57.
– volume: 31
  start-page: 264
  year: 1999
  end-page: 323
  ident: BIB4
  article-title: Data Clustering
  publication-title: ACM Comput. Surveys
– volume: 22
  start-page: 4
  year: 2000
  end-page: 37
  ident: BIB2
  article-title: Statistical pattern recognition
  publication-title: IEEE Trans. Pattern Anal. Mach. Intell.
– volume: 32
  start-page: 68
  year: 1999
  end-page: 75
  ident: BIB8
  article-title: CHAMELEON
  publication-title: IEEE Comput.
– volume: 18
  start-page: 375
  year: 2002
  end-page: 391
  ident: BIB15
  article-title: The new k-windows algorithm for improving the k-Means clustering algorithm
  publication-title: J. of Complexity
– reference: A. Hinneburg, D.A. Keim, An Efficient Approach to Clustering in Large Multimedia Data bases with Noise, American Association for Artificial Intelligence, Proceedings of the 1998 International Conference on Knowledge Discovery and Data Mining (KDD’98), pp. 58–65.
– reference: S. Guha, R. Rastogi, K. Shim, CURE: an efficient clustering algorithm for large data bases, Proceedings of the 1998 ACM SIGMOD International Conference on Management of Data, Vol. 27, No. 2, 1998, pp. 73–84.
– reference: .
– volume: 34
  start-page: 425
  year: 2001
  ident: 10.1016/j.patcog.2003.08.014_BIB14
  article-title: A novel algorithm for data clustering
  publication-title: Pattern Recognition
  doi: 10.1016/S0031-3203(00)00002-9
– volume: 15
  start-page: 1005
  year: 2002
  ident: 10.1016/j.patcog.2003.08.014_BIB26
  article-title: Data visualisation and manifold mappling using the ViSOM
  publication-title: Neural Networks
  doi: 10.1016/S0893-6080(02)00075-8
– ident: 10.1016/j.patcog.2003.08.014_BIB9
  doi: 10.1109/ICDE.1999.754967
– ident: 10.1016/j.patcog.2003.08.014_BIB11
  doi: 10.1080/01969727308546046
– ident: 10.1016/j.patcog.2003.08.014_BIB18
  doi: 10.1145/304182.304187
– ident: 10.1016/j.patcog.2003.08.014_BIB20
  doi: 10.1109/ICDE.1999.754914
– volume: 22
  start-page: 4
  issue: 1
  year: 2000
  ident: 10.1016/j.patcog.2003.08.014_BIB2
  article-title: Statistical pattern recognition
  publication-title: IEEE Trans. Pattern Anal. Mach. Intell.
  doi: 10.1109/34.824819
– year: 2001
  ident: 10.1016/j.patcog.2003.08.014_BIB3
– volume: 31
  start-page: 264
  issue: 3
  year: 1999
  ident: 10.1016/j.patcog.2003.08.014_BIB4
  article-title: Data Clustering
  publication-title: ACM Comput. Surveys
  doi: 10.1145/331499.331504
– volume: 32
  start-page: 68
  issue: 8
  year: 1999
  ident: 10.1016/j.patcog.2003.08.014_BIB8
  article-title: CHAMELEON
  publication-title: IEEE Comput.
  doi: 10.1109/2.781637
– ident: 10.1016/j.patcog.2003.08.014_BIB12
– volume: 18
  start-page: 375
  year: 2002
  ident: 10.1016/j.patcog.2003.08.014_BIB15
  article-title: The new k-windows algorithm for improving the k-Means clustering algorithm
  publication-title: J. of Complexity
  doi: 10.1006/jcom.2001.0633
– ident: 10.1016/j.patcog.2003.08.014_BIB7
  doi: 10.1145/276304.276312
– volume: 2
  start-page: 283
  year: 1998
  ident: 10.1016/j.patcog.2003.08.014_BIB13
  article-title: Extensions to the k-means algorithm for clustering large data sets with categorical values
  publication-title: Data Mining Knowledge Discovery
  doi: 10.1023/A:1009769707641
– ident: 10.1016/j.patcog.2003.08.014_BIB10
– ident: 10.1016/j.patcog.2003.08.014_BIB16
– year: 2001
  ident: 10.1016/j.patcog.2003.08.014_BIB1
– ident: 10.1016/j.patcog.2003.08.014_BIB6
  doi: 10.1145/235968.233324
– volume: 8
  start-page: 289
  year: 2000
  ident: 10.1016/j.patcog.2003.08.014_BIB21
  article-title: WaveCluster
  publication-title: VLDB J.
  doi: 10.1007/s007780050009
– ident: 10.1016/j.patcog.2003.08.014_BIB5
– ident: 10.1016/j.patcog.2003.08.014_BIB24
– ident: 10.1016/j.patcog.2003.08.014_BIB17
– ident: 10.1016/j.patcog.2003.08.014_BIB22
  doi: 10.1145/276304.276314
– ident: 10.1016/j.patcog.2003.08.014_BIB23
  doi: 10.1109/ICII.2001.983048
– ident: 10.1016/j.patcog.2003.08.014_BIB25
  doi: 10.1109/ICPR.1996.546732
– ident: 10.1016/j.patcog.2003.08.014_BIB19
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