Satellite image clustering and optimization using K-means and PSO

Particle swarm optimization (PSO) is a population based optimization technique, inspired by social behavior of animal and birds, is known to effectively solve large-scale nonlinear optimization problems. This paper presents a brief overview of the basic concepts of clustering techniques proposed in last four decades and a quick review of different similarity measure has been done. K-means is implemented to cluster satellite image of city Mumbai (India) and standard image such as mandrill and clown in HSV color space. PSO is used to optimize clusters results from k-means and within-cluster sums of point-to-centroid distances are measured. The results illustrate that our approach can produce more compact and optimized clusters than the K means alone.

[1]  Alexander Hinneburg,et al.  DENCLUE 2.0: Fast Clustering Based on Kernel Density Estimation , 2007, IDA.

[2]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

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

[4]  B.K. Tripathy,et al.  SDR: An algorithm for clustering categorical data using rough set theory , 2011, 2011 IEEE Recent Advances in Intelligent Computational Systems.

[5]  James M. Keller,et al.  A fuzzy K-nearest neighbor algorithm , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[6]  S. C. Johnson.  Hierarchical clustering schemes , 1967, Psychometrika.

[7]  J. Bezdek,et al.  FCM: The fuzzy c-means clustering algorithm , 1984 .

[8]  Pawan Lingras,et al.  Interval Set Clustering of Web Users with Rough K-Means , 2004, Journal of Intelligent Information Systems.

[9]  Douglas H. Fisher,et al.  Knowledge Acquisition Via Incremental Conceptual Clustering , 1987, Machine Learning.

[10]  R. Storn,et al.  Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series) , 2005 .

[11]  Peter J. Rousseeuw,et al.  Clustering by means of medoids , 1987 .

[12]  B. K. Tripathy,et al.  MMeR: an algorithm for clustering heterogeneous data using rough set theory , 2009 .

[13]  James C. Bezdek,et al.  Genetic algorithm guided clustering , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

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

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

[16]  Xiaohui Cui,et al.  Document Clustering Analysis Based on Hybrid PSO+K-means Algorithm , 2005 .

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

[18]  Ganapati Panda,et al.  A survey on nature inspired metaheuristic algorithms for partitional clustering , 2014, Swarm Evol. Comput..

[19]  Alok N. Choudhary,et al.  Adaptive Grids for Clustering Massive Data Sets , 2001, SDM.

[20]  Jennifer Blackhurst,et al.  MMR: An algorithm for clustering categorical data using Rough Set Theory , 2007, Data Knowl. Eng..

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

[22]  Lior Rokach,et al.  Data Mining And Knowledge Discovery Handbook , 2005 .

[23]  Andries Petrus Engelbrecht,et al.  Dynamic clustering using particle swarm optimization with application in image segmentation , 2006, Pattern Analysis and Applications.

[24]  Jim Z. C. Lai,et al.  Rough clustering using generalized fuzzy clustering algorithm , 2013, Pattern Recognit..