Paper Info
Title
Automatic Data Clustering Using Parameter Adaptive Harmony Search Algorithm and Its Application to Image Segmentation.
Abstract
In this paper, the problem of automatic data clustering is treated as the searching of optimal number of clusters so that the obtained partitions should be optimized. The automatic data clustering technique utilizes a recently developed parameter adaptive harmony search (PAHS) as an underlying optimization strategy. It uses real-coded variable length harmony vector, which is able to detect the number of clusters automatically. The newly developed concepts regarding "threshold setting" and "cutoff" are used to refine the optimization strategy. The assignment of data points to different cluster centers is done based on the newly developed weighted Euclidean distance instead of Euclidean distance. The developed approach is able to detect any type of cluster irrespective of their geometric shape. It is compared with four well-established clustering techniques. It is further applied for automatic segmentation of grayscale and color images, and its performance is compared with other existing techniques. For real-life datasets, statistical analysis is done. The technique shows its effectiveness and the usefulness.
Year
DOI
Venue
2016
10.1515/jisys-2015-0004
JOURNAL OF INTELLIGENT SYSTEMS
Keywords
Field
DocType
Harmony search algorithm,clustering,variance,meta-heuristics
Canopy clustering algorithm,CURE data clustering algorithm,Scale-space segmentation,Pattern recognition,Computer science,Segmentation-based object categorization,Image segmentation,Artificial intelligence,Harmony search,Cluster analysis,Metaheuristic
Journal
Volume
Issue
ISSN
25
4
0334-1860
Citations 
PageRank 
References 
2
0.36
17
Authors
3
Name
Order
Citations
PageRank
Vijay Kumar122921.59
Jitender Kumar Chhabra223120.56
Dinesh Kumar324745.04