An efficient supervised clustering using fuzzy logic

Abstract

In semi administered bunching is one of the vital errands and goes for gathering the information objects into classes (groups) to such an extent that the similitude of items inside bunches is high and the comparability of articles between bunches is Less. The dataset once in a while might be in blended nature that is it might comprise of both numeric and unmitigated sort of information. So two types of different data with characteristics. Due to the different in their qualities keeping in mind the end goal to gather these sorts of information it is ideal to utilize the troupe grouping strategy which utilizes divide and combine way to deal with take care of this issue. In this paper the different dataset is divide into numeric and categorical data set and clustered using both traditional and fuzzy logic algorithms. The output is combined with ensemble clustering and evaluated by both f-measure and entropy measure. It is found that using fuzzy clustering algorithms gives better results.