ABSTRACT
Clustering is the process of grouping a set of objects into classes of similar objects. Although definitions of similarity vary from one clustering model to another, in most of these models the concept of similarity is based on distances, e.g., Euclidean distance or cosine distance. In other words, similar objects are required to have close values on at least a set of dimensions. In this paper, we explore a more general type of similarity. Under the pCluster model we proposed, two objects are similar if they exhibit a coherent pattern on a subset of dimensions. For instance, in DNA microarray analysis, the expression levels of two genes may rise and fall synchronously in response to a set of environmental stimuli. Although the magnitude of their expression levels may not be close, the patterns they exhibit can be very much alike. Discovery of such clusters of genes is essential in revealing significant connections in gene regulatory networks. E-commerce applications, such as collaborative filtering, can also benefit from the new model, which captures not only the closeness of values of certain leading indicators but also the closeness of (purchasing, browsing, etc.) patterns exhibited by the customers. Our paper introduces an effective algorithm to detect such clusters, and we perform tests on several real and synthetic data sets to show its effectiveness.
- R. C. Agarwal, C. C. Aggarwal, and V. Parsad. Depth first generation of long patterns. In SIGKDD, 2000.]] Google ScholarDigital Library
- C. C. Aggarwal, C. Procopiuc, J. Wolf, P. S. Yu, and J. S. Park. Fast algorithms for projected clustering. In SIGMOD, 1999.]] Google ScholarDigital Library
- C. C. Aggarwal and P. S. Yu. Finding generalized projected clusters in high dimensional spaces. In SIGMOD, pages 70-81, 2000.]] Google ScholarDigital Library
- R. Agrawal, J. Gehrke, D. Gunopulos, and P. Raghavan. Authomatic subspace clustering of high dimensional data for data mining applications. In SIGMOD, 1998.]] Google ScholarDigital Library
- K. Beyer, J. Goldstein, R. Ramakrishnan, and U. Shaft. When is nearest neighbors meaningful. In Proc. of the Int. Conf. Database Theories, pages 217-235, 1999.]] Google ScholarDigital Library
- C. H. Cheng, A. W. Fu, and Y. Zhang. Entropy-based subspace clustering for mining numerical data. In SIGKDD, pages 84-93, 1999.]] Google ScholarDigital Library
- Y. Cheng and G. Church. Biclustering of expression data. In Proc. of 8th International Conference on Intelligent System for Molecular Biology, 2000.]] Google ScholarDigital Library
- P. D'haeseleer, S. Liang, and R. Somogyi. Gene expression analysis and genetic network modeling. In Pacific Symposium on Biocomputing, 1999.]]Google Scholar
- M. Ester, H. Kriegel, J. Sander, and X. Xu. A density-bsed algorithm for discovering clusters in large spatial databases with noise. In SIGKDD, pages 226-231, 1996.]]Google Scholar
- D. H. Fisher. Knowledge acquisition via incremental conceptual clustering. In Machine Learning, 1987.]] Google ScholarDigital Library
- K. Fukunaga. Introduction to Statistical Pattern Recognition. Academic Press, 1990.]] Google ScholarDigital Library
- H. V. Jagadish, J. Madar, and R. Ng. Semantic compression and pattern extraction with fascicles. In VLDB, pages 186-196, 1999.]] Google ScholarDigital Library
- R. S. Michalski and R. E. Stepp. Learning from observation: conceptual clustering. In Machine Learning: An Artificial Intelligence Approach, pages 331-363, 1983.]]Google Scholar
- F. Murtagh. A survey of recent hierarchical clustering algorithms. In The Computer Journal, 1983.]]Google ScholarCross Ref
- H. Nagesh, S. Goil, and A. Choudhary. Mafia: Efficient and scalable subspace clustering for very large data sets. Technical Report 9906-010, Northwestern University, 1999.]]Google Scholar
- R. T. Ng and J. Han. Efficient and effective clustering methods for spatial data mining. In VLDB, 1994.]] Google ScholarDigital Library
- J. Riedl and J. Konstan. Movielens dataset. In http://www.cs.umn.edu/Research/Group Lens.]]Google Scholar
- U. Shardanand and P. Maes. Social information filtering: Algorithms for automating 'word of mouth'. In Proceeding of ACM CHI, pages 210-217, 1995.]] Google ScholarDigital Library
- S. Tavazoie, J. Hughes, M. Campbell, R. Cho, and G. Church. Yeast micro data set. In http://arep.med.harvard.edu/biclustering/yeast.matrix, 2000.]]Google Scholar
- J. Yang, W. Wang, H. Wang, and P. S. Yu. Δ-clusters: Capturing subspace correlation in a large data set. In ICDE, pages 517-528, 2002.]] Google ScholarDigital Library
- T. Zhang, R. Ramakrishnan, and M. Livny. Birch: An efficient data clustering method for very large databases. In SIGMOD, pages 103-114, 1996.]] Google ScholarDigital Library
Index Terms
- Clustering by pattern similarity in large data sets
Recommendations
A Similarity Measure for Clustering Gene Expression Data
ICAA 2014: Proceedings of the First International Conference on Applied Algorithms - Volume 8321A similarity measure for gene expression data should give the shapes of the patterns of the gene expression data and should be less susceptible to outliers. In this paper, we present a similarity measure for clustering gene expression time series data. ...
Clustering by pattern similarity
The task of clustering is to identify classes of similar objects among a set of objects. The definition of similarity varies from one clustering model to another. However, in most of these models the concept of similarity is often based on such metrics ...
Clustering of gene expression data based on shape similarity
Special issue on applications of signal procesing techniques to bioinformatics, genomics, and proteomicsA method for gene clustering from expression profiles using shape information is presented. The conventional clustering approaches such as K-means assume that genes with similar functions have similar expression levels and hence allocate genes with ...
Comments