A New Algorithm Based on Improved Artificial Fish Swarm Algorithm for Data Clustering
Artificial Fish Swarm Algorithm (AFSA) is one of the state-of-the-art swarm intelligence approaches that is widely used for optimization purposes. On the other hand, data clustering is an unsupervised classification technique which has been addressed by researchers in many disciplines and in many contexts. The contribution toward this study is twofold. First, weak points of standard AFSA including lack of using previous experiences of AFs during optimization process, lack of existing balance between exploration and exploitation and high computational load were investigated in order to present a New Artificial Fish Swarm Algorithm (NAFSA). For resolving the weak points, functional behaviors and the overall procedure of AFSA have been improved. In addition, some parameters are eliminated and several supplementary parameters are added. Subsequently, a hybrid clustering algorithm was proposed based on NAFSA and k-means approaches. This combination leads to maximum utilization of the involved approaches for data clustering. The proposed methods were evaluated on several datasets and its efficiency was compared with that of several state-of-art algorithms in this domain. Results showed high efficiency of the proposed algorithm.
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.thentic information.