• JITENDRASINH RAULJI
• DHARMESH DHANGAR

Data mining provides facility that can help to finds useful patterns from large size of data. And if we talk about clustering, it is important task of data mining. Clustering is a process of grouping similar objects into a group. Object in same cluster are more similar to each other than the objects in different clusters. Fuzzy c-means (FCM) algorithm is also an important clustering technique. But the Fuzzy c-mean is noises sensitive and is easily struck at local minima. The possibilistic c-means (PCM) algorithm solves the noise sensitivity problem of FCM algorithm. The Membership function is used by the Possibilistic c-means algorithm to illustrate the degree of belonging. The component produced by the PCM communicates to a dense region in the data set. All clusters are independent of the other clusters in the PCM approach. Particle swarm optimization is a heuristic global optimization method and also an optimization algorithm, which is based on swarm intelligence. Here, In this paper I tried to compare outcomes of PCM and PSO with some dataset.

Clustering, Fuzzy c-mean, PCM, PSO, Data mining, data clustering,, Possibilistic c-means, Fuzzy possibilistic c-means.

"A COMPARISION ON FUZZY C MEAN ALGORITHM AND PARTICLE SWARM OPTIMIZATION ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 4, page no.136-138, April-2019, Available :http://www.jetir.org/papers/JETIR1904727.pdf

"A COMPARISION ON FUZZY C MEAN ALGORITHM AND PARTICLE SWARM OPTIMIZATION ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 4, page no. pp136-138, April-2019, Available at : http://www.jetir.org/papers/JETIR1904727.pdf