Modified Fuzzy C Means Clustering To Study the Willingness Maximization Using Discrete Multi-Valued Particle Swarm Optimization (DPSO) For Social Activity Planning

In the recent work demonstrate that a person is ready to join a social group action if the particular activity is attractive, and if a number of close friend’s moreover also join the activity as companions. From the survey it shows with the intention of the interests of a person and the social rigidity between friends is able to be successfully derived and mined from social networking websites. On the other hand, even by means of the above mentioned two categories of information extensively presented, social group activities still necessitate to be corresponding physically ,and the development is tedious and timeconsuming on behalf of users, particularly for a large social group activity, because of the several difficulty of social connectivity and the variety of feasible interests between friends .To solve above mentioned problems in this research work presents a new Modified Fuzzy C Means (MFCM) for the grouping of similar users particularly for large s ocial group activity. MFCM clustering method suggests prospective attendees of a social group activity, which might be extremely helpful, designed for social networking websites as a value-added service. For this grouping of similar user activities first need to specify a new problem, named Willingness mAximization for Social grOup (WASO). To solve the problem of WASO points out that the solution obtained by Discrete multi-valued Particle Swarm Optimization (DPSO) is expected to be attentive in a local optimal solution. Thus, new DPSO randomized algorithm to successfully and proficiently solve the problem. Specified the presented computational budgets, the proposed WASO-DPSO algorithmic capable to optimally assign the resources and discover a solution by means of an approximation ratio.