Andrew K.C. Wong
ABSTRACT
In business and industry today, large databases with mixed data types (continuous and categorical) are very common. There are great needs to discover patterns from them for knowledge interpretation and understanding. In the past, for classification, this problem is solved as a discrete data problem by first discretizing the continuous data based on the class-attribute interdependence relationship. However, so far no proper solution exists when class information is unavailable. Hence, important pattern post-processing tasks such as pattern clustering and summarization cannot be applied to mixed-mode data. This paper presents a new method for solving the problem. It is based on two essential concepts. (1) Though class information is absent, yet for a correlated dataset, the attribute with the strongest interdependence with others in the group can be used to drive the discretization of the continuous data. (2) For a large database, correlated attribute groups must first be obtained by attribute clustering before (1) can be applied. Based on (1) and (2), pattern discovery methods are developed for mixed-mode data. Extensive experiments using synthetic and real world data were conducted to validate the usefulness and effectiveness of the proposed method.
- Agrawal, R., Ghost, S., Imielinski, T., Iyer, B., and Swami, A. 1992. An interval classifier for database mining applications. In Proc. Int. Conf Very L. 560--573. Google Scholar
Digital Library
- Alon, U., Barkai, N., Notterman, D. A., Gish, K., Ybarra, S., Mack, D., and Levine, A. J. 1999. Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. In Proc. of the National Academy of Sciences of the United States of America. 96, 12, 6745--6750.Google ScholarCross Ref
- Asuncion, A., and Newman, D. J. 2007. UCI machine learning repository. Irvine, CA: University of California, School of Information and Computer Science. DOI= http://archive.ics.uci.edu/ml/.Google Scholar
- Au, W. H., Chan, K. C. C., and Yao, X. 2003. A novel evolutionary data mining algorithm with applications to churn prediction. IEEE T. Evolut. Comput. 7, 6 (Dec. 2003), 532--545. Google ScholarDigital Library
- Au, W. H., Chan, K. C. C., Wong, A. K. C., and Wang, Y. 2005. Attribute clustering for grouping, selection, and classification of gene expression data. IEEE-ACM T. Comput. Bi. 2, 2 (Apr. 2005), 83--101. Google ScholarDigital Library
- Ben-Dor, A., Bruhn, L., Friedman, N., Nachman, I., Schummer, M., and Yakhini, Z. 2000. Tissue classification with gene expression profiles. In Proceedings of the Fourth Annual International Conference on Computational Molecular Biology. Google ScholarDigital Library
- Chau, T., and Wong, A. K. C. 1999. Pattern discovery by residual analysis and recursive partitioning. IEEE T. Knowl. Data. En. 11, 6 (Nov. 1999), 833--854. Google ScholarDigital Library
- Ching, J. Y., Wong, A. K. C., and Chan, K. C. C. 1995. Class-dependent discretization for inductive learning from continuous and mixed-mode data. IEEE T. Pattern Anal. 17, 7(Jul. 1995), 631--641. Google ScholarDigital Library
- Chiu, D., Wong, A. C. K., and Cheung, B. 1990. Information discovery through hierarchical maximum entropy. J. Exp. Theor. Artif. In. 2, 117--129.Google ScholarCross Ref
- Ho, K. M., and Scott, P. D. 1997. Zeta: A global method for discretization of continuous variables. In D. Heckerman, H. Mannila, D. Pregibon, and R. Uthurusamy, editors, Knowledge Discovery and Data Mining, AAAI Press. 191--194.Google Scholar
- Kohavi, R., John, G., Long, R., Manley, D., and Pfleger, K. 1994. Mlc++: a machine learning library in c. In Proc Int. C Tools Art.Google Scholar
- Kurgan, L., and Cios, K. J. 2001. Discretization algorithm that uses class-attribute interdependence maximization. In Proceedings of the 2001 International Conference on Artificial Intelligence (IC-AI 2001), 980--987.Google Scholar
- Liu, H., Hussain, F., Tan, C. L., and Dash, M. 2002. Discretization: an enabling technique. Data Min. Knowl. Disc. 6, 4 (Oct. 2002), 393--423. Google ScholarDigital Library
- Liu, L., Wong, A. K. C., and Wang, Y. 2004. A global optimal algorithm for class-dependent discretization of continuous data. Intell. Data Anal. 8, 2, 151--170. Google ScholarDigital Library
- Quinlan, J. R. 1993. C4.5: Programs for Machine Learning. Morgan Kauffman, San, Mateo CA. Google ScholarDigital Library
- Wang, C. C., and Wong, A. K. C. 1979. Classification of discrete-valued data with feature space transformation. IEEE T. Automat. Contr. 24, 3, 434--437.Google ScholarCross Ref
- Wang, Y., and Wong, A. K. C. 2010. Discover*e. Pattern Discovery Technologies. DOI= http://www.patterndiscovery.com.Google Scholar
- Wang, Y., and Wong, A. K. C. 2003. From association to classification: inference using weight of evidence. IEEE T. Knowl. Data. En. 15, 3, 914--925, 200. Google ScholarDigital Library
- Wong, A. K. C., and Wang, Y. 2003. Pattern discovery: a data driven approach to decision support. IEEE T Syst. Man Cy. C, 33, 1, 114--124. Google ScholarDigital Library
- Wong, A. K. C., and Chiu, D. K. Y. 1987. Synthesizing statistical knowledge from incomplete mixed-mode data. IEEE T. Pattern Anal. 9, 8 (Nov. 1987), 796--805. Google ScholarDigital Library
- Wong, A. K. C., and Liu, T. S. 1975. Typicality, diversity and feature patterns of an ensemble. IEEE T. Comput. 24, 2 (Feb. 1975), 158--181. Google ScholarDigital Library
- Wong, A. K. C., and Wang, Y. 1997. High order pattern discovery from discrete-valued data. IEEE T. Knowl. Data. En. 9, 6 (Nov. 1997), 877--893. Google ScholarDigital Library
- Wong, A. K. C., Chiu D. K. Y., and Huang, W. 2001. A discrete-valued clustering algorithm with applications to bimolecular data. Information Sciences, 139, 1--2 (Nov. 2001), 97--112. Google ScholarDigital Library
- Wong, A. K. C., Liu, T. S., and Wang, C. C. 1976. Statistical analysis of residue variability in Cytochrome C. J. Mol. Biol. 102, 2(Apr. 1976), 287--295.Google ScholarCross Ref
- Wong, A. K. C., and Li, G. C. L. 2008. Simultaneous pattern and data clustering for pattern cluster analysis. IEEE T. Knowl. Data. En. 20, 7 (Jul. 2008), 911--923. Google ScholarDigital Library
- Wong, A. K. C., and Li, G. C. L. 2010. Association pattern analysis for pattern pruning, pattern clustering and summarization, to appear in Journal of Knowledge and Information Systems, 2010.Google Scholar
Index Terms
- Pattern discovery for large mixed-mode database
Recommendations
Attribute Clustering for Grouping, Selection, and Classification of Gene Expression Data
This paper presents an attribute clustering method which is able to group genes based on their interdependence so as to mine meaningful patterns from the gene expression data. It can be used for gene grouping, selection, and classification. The ...
An Integrated Framework for Visualized and Exploratory Pattern Discovery in Mixed Data
Data mining uncovers hidden, previously unknown, and potentially useful information from large amounts of data. Compared to the traditional statistical and machine learning data analysis techniques, data mining emphasizes providing a convenient and ...
A constrained frequent pattern mining system for handling aggregate constraints
IDEAS '12: Proceedings of the 16th International Database Engineering & Applications SysmposiumFrequent pattern mining searches data for sets of items that are frequently co-occurring together. Most of algorithms find all the frequent patterns. However, there are many real-life situations in which users is interested in only some small portions ...
Comments