基于空间结构的符号数据仿射传播算法<sup>*</sup>

doi:10.16451/j.cnki.issn1003-6059.201612009

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Abstract Constructing a reasonable similarity measure is difficult due to the lack of clear space structure in categorical data. Therefore, numerical clustering algorithms can hardly be extended to categorical data clustering. In this paper, a representation method for transforming the categorical data into numerical data is introduced. The similarity between samples is reconstructured and the structure feature of the original categorical data is maintained in the reconstruction process. Based on the data representation method, the affinity propagation(AP) clustering algorithm is migrated to the categorical data clustering. A space structure based AP algorithm for categorical data(SBAP) is proposed. Experimental results on several categorical datasets from the UCI dataset show that the proposed method makes AP algorithm deal with the categorical data clustering problem effectively with a significant improvement in performance.

Key words： Clustering Categorical Data Affinity Propagation (AP) Space Structure Similarity

Received: 13 May 2016

ZTFLH:

TP 181

Fund:Supported by National Natural Science Foundation of China (No.61432011,U1435212,61322211), Program for New Century Excellent Talents in University of Ministry Education of China (No.NCET-12-1031), Specialized Research Fund for the Doctoral Program of Higher Education (No.20121401110013), Program for the Top Young Academic Leaders of Higher Learning Institutions of Shanxi Province (No.20120301)

About author:: (WANG Qi, born in 1979, Ph.D., lecturer. His research interests include data mining and knowledge discovery.)
(QIAN Yuhua(Corresponding author), born in 1976, Ph.D., professor. His research interests include artificial intelligence, data mining and machine learning.)
(LI Feijiang, born in 1990, Ph.D.candidate. His research interests include data mining and knowledge discovery.)

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Cite this article:

WANG Qi,QIAN Yuhua,LI Feijiang. Space Structure Based Affinity Propagation Algorithm for Categorical Data[J]. , 2016, 29(12): 1132-1139.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201612009 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2016/V29/I12/1132

[1] CHMIELEWSKI M R, GRZYMALA-BUSSE J W. Global Discretization of Continuous Attributes as Preprocessing for Machine Learning. International Journal of Approximate Reasoning, 1996, 15(4): 319-331.
[2] DASH M, LIU H. Consistency-Based Search in Feature Selection. Artificial Intelligence, 2003, 151(1/2): 155-176.
[3] KOHAVI R, JOHN G H. Wrappers for Feature Subset Selection. Artificial Intelligence, 1997, 97(1/2): 273-324.
[4] ZHOU Z H. Three Perspectives of Data Mining. Artificial Intelligence, 2003, 143(1): 139-146.
[5] MACQUEEN J. Some Methods for Classification and Analysis of Multivariate Observations // Proc of the 5th Berkeley Symposium on Mathematical Statistics and Probability. Berkeley, USA: University of California Press, 1967: 281-297.
[6] MITCHELL T M. Machine Learning. New York, USA: McGraw-Hill, 1997.
[7] CHEN X Q, PENG H, HU J S. K-Medoids Substitution Clustering Method and a New Clustering Validity Index Method // Proc of the 6th World Congress on Intelligent Control and Automation. New York, USA: IEEE, 2006: 5896-5900.
[8] GUHA S, RASTOGI R, SHIM K. CURE: An Efficient Clustering Algorithm for Large Databases. Information Systems, 2001, 26(1): 35-58.
[9] GUHA S, RASTOGI R, SHIM K. ROCK: A Robust Clustering Algorithm for Categorical Attributes // Proc of the 15th International Conference on Data Engineering. Washington, USA: IEEE, 1999: 512-521.
[10] ZHANG T, RAMAKRISHNAN R, LIVNY M. BIRCH: An Efficient Data Clustering Method for Very Large Databases // Proc of the ACM SIGMOD International Conference on Management of Data. Menlo Park, USA: AAAI Press, 1996: 103-114.
[11] ESTER M, KRIEGEL H P, SANDER J, et al. A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise // Proc of the 2nd International Conference on Knowledge Discovery and Data Mining. Menlo Park, USA: AAAI Press, 1996: 226-231.
[12] ANKERST M, BREUNIG M M, KRIEGEL H P, et al. OPTICS: Ordering Points to Identify the Clustering Structure // Proc of the ACM SIGMOD International Conference on Management of Data. Menlo Park, USA: AAAI Press, 1999: 49-60.
[13] HINNEBURG A, KEIM D A. An Efficient Approach to Clustering in Large Multimedia Databases with Noise // Proc of the 4th International Conference on Knowledge Discovery and Data Mining. Menlo Park, USA: AAAI Press, 1998: 58-65.
[14] WANG W, YANG J, MUNTZ R. STING: A Statistical Information Grid Approach to Spatial Data Mining[C/OL]. [2016-05-21]. http://www.vldb.org/conf/1997/P186.PDF.
[15] PARSONS L, HAQUE E, LIU H. Subspace Clustering for High Dimensional Data: A Review. ACM SIGKDD Explorations Newsletter, 2004, 6(1): 90-105.
[16] RALAM B H. A Conceptual Version of the k-means Algorithm. Pattern Recognition Letters, 1995, 16(11): 1147-1157.
[17] HUANG Z X. Extensions to the k-means Algorithm for Clustering Large Data Sets with Categorical Values. Data Mining and Know-ledge Discovery, 1998, 2(3): 283-304.
[18] CHATURVEDI A, GREEN P E, CAROLL J D. k-modes Clus-tering. Journal of Classification, 2001, 18(1): 35-55.
[19] HUANG Z X, NG M K. A Fuzzy k-modes Algorithm for Clustering Categorical Data. IEEE Trans on Fuzzy Systems, 1999, 7(4): 446-452.
[20] GAN G, WU J, YANG Z. A Genetic Fuzzy k-modes Algorithm for Clustering Categorical Data. Expert Systems with Applications, 2008, 36(2): 1615-1620.
[21] GANTI V, GEHRKE J, RAMAKRISHNAN R. CACTUS-Clus-tering Categorical Data Using Summaries // Proc of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM, 1999: 73-83.
[22] BARBAR D, LI Y, COUTO J. COOLCAT: An Entropy-Based Algorithm for Categorical Clustering // Proc of the 11th International Conference on Information and Knowledge Management. New York, USA: ACM, 2002: 582-589.
[23] FISHER D H. Knowledge Acquisition via Incremental Conceptual Clustering. Machine Learning, 1987, 2(2): 139-172.
[24] FREY B J, DUECK D. Clustering by Passing Messages between Data Points. Science, 2007, 315(5814): 972-976.
[25] QIAN Y, LI F, LIANG J, et al. Space Structure and Clustering of Categorical Data. IEEE Trans on Neural Networks and Learning Systems, 2015. DOI: 10.1109/TNNLS.2015.2451151.
[26] CHAN E Y, CHING W K, NG M K, et al. An Optimization Algorithm for Clustering Using Weighted Dissimilarity Measures. Pattern Recognition, 2004, 37(5): 943-952.
[27] BAI L, LIANG J, DANG C, et al. The Impact of Cluster Representatives on the Convergence of the k-modes Type Clustering. IEEE Trans on Pattern Analysis and Machine Intelligence, 2013, 35(6): 1509-1522.