A Point Symmetry-Based Clustering Technique for Automatic Evolution of Clusters

Authors:
Sanghamitra Bandyopadhyay;Sriparna Saha
Affiliations:
Indian Statistical Institute, Kolkata;Indian Statistical Institute, Kolkata
Venue:
IEEE Transactions on Knowledge and Data Engineering
Year:
2008

Citing 0
Cited 17

Performance evaluation of density-based clustering methods

Information Sciences: an International Journal
A symmetry based multiobjective clustering technique for automatic evolution of clusters

Pattern Recognition
A Point Symmetry-Based Automatic Clustering Approach Using Differential Evolution

ISICA '09 Proceedings of the 4th International Symposium on Advances in Computation and Intelligence
A robust dynamic niching genetic algorithm with niche migration for automatic clustering problem

Pattern Recognition
MR Brain Image Segmentation Using A Multi-seed Based Automatic Clustering Technique

Fundamenta Informaticae
A new multiobjective clustering technique based on the concepts of stability and symmetry

Knowledge and Information Systems
A new multi-objective technique for differential fuzzy clustering

Applied Soft Computing
Automatic MR brain image segmentation using a multiseed based multiobjective clustering approach

Applied Intelligence
Efficient parallel algorithm for pixel classification in remote sensing imagery

Geoinformatica
Determining the number of clusters using information entropy for mixed data

Pattern Recognition
Some connectivity based cluster validity indices

Applied Soft Computing
Automatic clustering based on invasive weed optimization algorithm

SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part II
MR Brain Image Segmentation Using A Multi-seed Based Automatic Clustering Technique

Fundamenta Informaticae
An extensive comparative study of cluster validity indices

Pattern Recognition
A generalized automatic clustering algorithm in a multiobjective framework

Applied Soft Computing
A powerful hybrid clustering method based on modified stem cells and Fuzzy C-means algorithms

Engineering Applications of Artificial Intelligence
Gene expression data clustering using a multiobjective symmetry based clustering technique

Computers in Biology and Medicine

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this article, a new symmetry based genetic clustering algorithm is proposed which automatically evolves the number of clusters as well as the proper partitioning from a data set. Strings comprise both real numbers and the don't care symbol in order to encode a variable number of clusters. Here, assignment of points to different clusters are done based on a point symmetry based distance rather than the Euclidean distance. A newly proposed point symmetry based cluster validity index, {\em Sym}-index, is used as a measure of the validity of the corresponding partitioning. The algorithm is therefore able to detect both convex and non-convex clusters irrespective of their sizes and shapes as long as they possess the point symmetry property. Kd-tree based nearest neighbor search is used to reduce the complexity of computing point symmetry based distance. A proof on the convergence property of variable string length GA with point symmetry based distance clustering (VGAPS-clustering) technique is also provided. The effectiveness of VGAPS-clustering compared to variable string length Genetic K-means algorithm (GCUK-clustering) and one recently developed weighted sum validity function based hybrid niching genetic algorithm (HNGA-clustering) is demonstrated for nine artificial and five real-life data sets.