A Hybrid Clustering Method Based on Improved Artificial Bee Colony and Fuzzy C-Means Algorithm
Data clustering is an important data mining technique to create groups (clusters) of objects, in such a way that objects in one cluster are very similar and objects in different clusters are quite distinct. Fuzzy c-means (FCM) algorithm is a popular data clustering method that works according to the fuzzy membership between data points and cluster centers. However, it has possibilities of convergence to local minima. Artificial Bee Colony (ABC) algorithm is a swarm based algorithm inspired by intelligent foraging behavior of honey bees. In order to make use of merits of both algorithms, a hybrid algorithm (IABCFCM) based on improved ABC and FCM algorithms is proposed in this paper. The IABCFCM algorithm helps the FCM clustering escape from local optima and provides better experimental results on the well known data sets.
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.