An Efficient Approach to Higher Dimensional Data Clustering

Conventional clustering algorithms are not so efficient on higher dimensional data due to the problem of dimensionality curse. To address this issue searching for clusters in appropriate subspaces is performed. But searching all possible subspaces is exhaustive. In this paper we propose an efficient approach to effectively find the relevant subspaces in high dimensional data and apply clustering in those subspaces. Experiments are conducted on real and synthetic data sets and compared with other approaches and our approach is able to return good clustering results.

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

[2]  Raymond Chi-Wing Wong,et al.  Projective clustering by histograms , 2005, IEEE Transactions on Knowledge and Data Engineering.

[3]  C.M. Lim,et al.  Experimental results on a supervised fuzzy logic control scheme for a DC motor , 1999, FUZZ-IEEE'99. 1999 IEEE International Fuzzy Systems. Conference Proceedings (Cat. No.99CH36315).

[4]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .

[5]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[6]  Pavel Berkhin,et al.  A Survey of Clustering Data Mining Techniques , 2006, Grouping Multidimensional Data.

[7]  Jiong Yang,et al.  STING: A Statistical Information Grid Approach to Spatial Data Mining , 1997, VLDB.

[8]  Ahmed Rubaai,et al.  Experimental verification of a hybrid fuzzy controller for a high performance brushless DC drive system , 1999, Conference Record of the 1999 IEEE Industry Applications Conference. Thirty-Forth IAS Annual Meeting (Cat. No.99CH36370).

[9]  Charu C. Aggarwal,et al.  Re-designing distance functions and distance-based applications for high dimensional data , 2001, SGMD.

[10]  Tian Zhang,et al.  BIRCH: an efficient data clustering method for very large databases , 1996, SIGMOD '96.

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

[12]  Philip S. Yu,et al.  Redefining Clustering for High-Dimensional Applications , 2002, IEEE Trans. Knowl. Data Eng..

[13]  N. Inanc,et al.  DC motor control by using computer based fuzzy technique , 1999, APEC '99. Fourteenth Annual Applied Power Electronics Conference and Exposition. 1999 Conference Proceedings (Cat. No.99CH36285).

[14]  Tae-Bin Im,et al.  A low cost speed control system of brushless DC motor using fuzzy logic , 1999, 1999 Information, Decision and Control. Data and Information Fusion Symposium, Signal Processing and Communications Symposium and Decision and Control Symposium. Proceedings (Cat. No.99EX251).

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

[16]  G. Deeb,et al.  Experimental comparative analysis of conventional, fuzzy logic, and adaptive fuzzy logic controllers , 1999, Conference Record of the 1999 IEEE Industry Applications Conference. Thirty-Forth IAS Annual Meeting (Cat. No.99CH36370).

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

[18]  Emil Levi,et al.  A comparative analysis of fuzzy logic and PI speed control in high-performance AC drives using experimental approach , 2000 .

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

[20]  D. A. Linkens,et al.  Similarity-based rules reduction of a fuzzy logic controller for a PMDC motor drive system , 1997, Proceedings of 12th IEEE International Symposium on Intelligent Control.