Paper Info
Title
A New Kernel based Hybrid c-Means Clustering Model
Abstract
A possibilistic approach was initially proposed for c-means clustering. Although the possibilistic approach is sound, this algorithm tends to find identical clusters. To overcome this shortcoming, a possibilistic fuzzy c-means algorithm (PFCM) was proposed which produced memberships and possibilities simultaneously, along with the cluster centers. PFCM addresses the noise sensitivity defect of fuzzy c-means (FCM) and overcomes the coincident cluster problem of possibilistic c means (PCM). Here we propose a new model called Kernel based hybrid c means clustering (KPFCM) where PFCM is extended by adopting a Kernel induced metric in the data space to replace the original Euclidean norm metric. Numerical examples show that our model gives better results than the previous models.
Year
DOI
Venue
2007
10.1109/FUZZY.2007.4295583
FUZZ-IEEE
Keywords
Field
DocType
pattern clustering,possibility theory,Euclidean norm metric,fuzzy c-means algorithm,hybrid c-means clustering model,possibilistic c means algorithm
Kernel (linear algebra),Pattern recognition,Computer science,Support vector machine,Euclidean distance,Fuzzy logic,Possibility theory,Fuzzy set,Artificial intelligence,Induced metric,Cluster analysis,Machine learning
Conference
ISSN
ISBN
Citations 
1098-7584 E-ISBN : 1-4244-1210-2
1-4244-1210-2
3
PageRank 
References 
Authors
0.45
4
2
Name
Order
Citations
PageRank
Meena Tushir1122.36
Smriti Srivastava213719.60