Ant based rule mining with parallel fuzzy cluster

Ant-based techniques, in the computer sciences, are designed those who take biologicalinspirations on the behavior of these social insects. Data clustering techniques are classification algorithmsthat have a wide range of applications, from Biology to Image processing and Data presentation. Since reallife ants do perform clustering and sorting of objects among their many activities, we expect that an study ofant colonies can provide new insights for clustering techniques. The aim of clustering is to separate a set ofdata points into self-similar groups such that the points that belong to the same group are more similar thanthe points belonging to different groups. Each group is called a cluster. Data may be clustered using aniterative version of the Fuzzy C means (FCM) algorithm, but the draw back of FCM algorithm is that it is verysensitive to cluster center initialization because the search is based on the hill climbing heuristic. The antbased algorithm provides a relevant partition of data without any knowledge of the initial cluster centers. Inthe past researchers have used ant based algorithms which are based on stochastic principles coupled withthe k-means algorithm. The proposed system in this work use the Fuzzy C means algorithm as thedeterministic algorithm for ant optimization. The proposed model is used after reformulation and thepartitions obtained from the ant based algorithm were better optimized than those from randomly initializedhard C Means. The proposed technique executes the ant fuzzy in parallel for multiple clusters. This wouldenhance the speed and accuracy of cluster formation for the required system problem.