EFFICIENT UNSUPERVISED CLUSTERING THROUGH INTELLIGENT OPTIMIZATION

A novel methodology for unsupervised data clustering based on Evolutionary Computation, named “Intelligent Unsupervised Clustering” (IUC) is introduced. IUC searches for the “optimal clusters’ representatives” using Evolutionary Algorithms (EAs) and utilising a Window Density Function (WDF) as an objective function. EAs ensure that the representative is posed in a region of points of high density. IUC aims in finding a highly dense hyperrectangle around the cluster’s representative, that captures a part of cluster. Therefore, IUC uses a windowing technique and gradually enlarges a window, which is centered on the best individual generated from the EA. This process continues until the increase of the value of WDF does not change “drastically”. The whole process is repeated on the unclustered data, until all the clusters are discovered. The quality of clustering, delivered by the IUC, is compared with well-known clustering algorithms and the experimental results illustrate its efficiency and accuracy.

[1]  Andries P. Engelbrecht,et al.  Computational Intelligence: An Introduction , 2002 .

[2]  C. H. Chen,et al.  Handbook of Pattern Recognition and Computer Vision , 1993 .

[3]  Michael N. Vrahatis,et al.  The New k-Windows Algorithm for Improving the k-Means Clustering Algorithm , 2002, J. Complex..

[4]  Swagatam Das,et al.  Automatic Clustering Using an Improved Differential Evolution Algorithm , 2007 .

[5]  Michael J. A. Berry,et al.  Data mining techniques - for marketing, sales, and customer support , 1997, Wiley computer publishing.

[6]  B. Ripley,et al.  Pattern Recognition , 1968, Nature.

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

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

[9]  C. Perna,et al.  Mathematical and statistical methods in insurance and finance , 2008 .

[10]  Dimitris K. Tasoulis,et al.  Improving the orthogonal range search k-windows algorithm , 2002, 14th IEEE International Conference on Tools with Artificial Intelligence, 2002. (ICTAI 2002). Proceedings..

[11]  Sandra Paterlini,et al.  Differential evolution and particle swarm optimisation in partitional clustering , 2006, Comput. Stat. Data Anal..

[12]  Dimitris K. Tasoulis,et al.  Unsupervised clustering in mRNA expression profiles , 2006, Comput. Biol. Medicine.

[13]  Anil K. Jain,et al.  Algorithms for Clustering Data , 1988 .

[14]  Dimitris K. Tasoulis,et al.  Financial forecasting through unsupervised clustering and neural networks , 2006, Oper. Res..

[15]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[16]  Phipps Arabie,et al.  AN OVERVIEW OF COMBINATORIAL DATA ANALYSIS , 1996 .

[17]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[18]  Marc Teboulle,et al.  Grouping Multidimensional Data - Recent Advances in Clustering , 2006 .

[19]  G H Ball,et al.  A clustering technique for summarizing multivariate data. , 1967, Behavioral science.

[20]  Narendra Ahuja,et al.  Advances in Image Understanding: A Festschrift for Azriel Rosenfeld , 1996 .

[21]  Jiawei Han,et al.  CLARANS: A Method for Clustering Objects for Spatial Data Mining , 2002, IEEE Trans. Knowl. Data Eng..

[22]  Dimitris K. Tasoulis,et al.  The new window density function for efficient evolutionary unsupervised clustering , 2005, 2005 IEEE Congress on Evolutionary Computation.

[23]  Joydeep Ghosh,et al.  Similarity-Based Text Clustering: A Comparative Study , 2006, Grouping Multidimensional Data.

[24]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[25]  Maoguo Gong,et al.  Density-Sensitive Evolutionary Clustering , 2007, PAKDD.

[26]  A. Engelbrecht,et al.  Self-Adaptive Differential Evolution Methods for Unsupervised Image Classification , 2006, 2006 IEEE Conference on Cybernetics and Intelligent Systems.

[27]  Andries Petrus Engelbrecht,et al.  An overview of clustering methods , 2007, Intell. Data Anal..