A modified fuzzy possibilistic C-means for context data clustering toward efficient context prediction

Context prediction is useful for energy saving and hence eco-efficient context-aware service by increasing the interval of context sensing. One way of predicting context is to recognize context patterns in an accurate manner. Traditionally, clustering method has been widely used in pattern recognition, as well as image processing and data analysis. Clustering aims to organize a collection of data items into a specific number of clusters, such that the data items within a cluster are more similar to each other than they are items in the other clusters. In this paper, a modified fuzzy possibilistic clustering algorithm is proposed based on the conventional Fuzzy Possibilistic C-means (FPCM) to obtain better quality clustering results. To show the feasibility and performance of the proposed method, numerical simulation is performed in an actual amusement park setting. The results of the numerical simulation shows that the proposed clustering algorithm gives more accurate clustering results than the FCM and FPCM methods.