PCFA: mining of projected clusters in high dimensional data using modified FCM algorithm

Data deals with the specific problem of partitioning a group of objects into a fixed number of subsets, so that the similarity of the objects in each subset is increased and the similarity across subsets is reduced. Several algorithms have been proposed in the literature for clustering, where k-means clustering and Fuzzy C-Means (FCM) clustering are the two popular algorithms for partitioning the numerical data into groups. But, due to the drawbacks of both categories of algorithms, recent researches have paid more attention on modifying the clustering algorithms. In this paper, we have made an extensive analysis on modifying the FCM clustering algorithm to overcome the difficulties possessed by the k-means and FCM algorithms over high dimensional data. According to, we have proposed an algorithm, called Projected Clustering based on FCM Algorithm (PCFA). Here, we have utilized the standard FCM clustering algorithm for sub-clustering high dimensional data into reference centroids. The matrix containing the reference values is then fed as an input to the modified FCM algorithm. Finally, experimentation is carried out on the very large dimensional datasets obtained from the benchmarks data repositories and the performance of the PCFA algorithm is evaluated with the help of clustering accuracy, memory usage and the computation time. The evaluation results showed that, the PCFA algorithm shows approximately 20% improvement in the execution time and 50% improvement in memory usage over the PCKA algorithm.