Density-Based Clustering Using Variable Kernel and Maximum Entropy Principle
In this paper, we introduce a new data clustering approach. It articulates essentially on the idea that each center is characterized by a special values of density as well as distance. The variable kernel estimator is the key element in this study. Our proposed method employs the entropy to find out the optimal bandwidth, which is crucial in density estimation. Data are previously mapped through kernel function before performing principal component analysis, which is efficient method for nonlinear patterns determination. The resulting data are reduced to lower-dimension feature space where only the efficient features are selected. Experiments divulge our proposed approach efficiency.
Disclaimer/Regarding indexing issue:
We have provided the online access of all issues and papers to the indexing agencies (as given on journal web site). It’s depend on indexing agencies when, how and what manner they can index or not. Hence, we like to inform that on the basis of earlier indexing, we can’t predict the today or future indexing policy of third party (i.e. indexing agencies) as they have right to discontinue any journal at any time without prior information to the journal. So, please neither sends any question nor expects any answer from us on the behalf of third party i.e. indexing agencies.Hence, we will not issue any certificate or letter for indexing issue. Our role is just to provide the online access to them. So we do properly this and one can visit indexing agencies website to get the authentic information. Also: DOI is paid service which provided by a third party. We never mentioned that we go for this for our any journal. However, journal have no objection if author go directly for this paid DOI service.