International Journal of Computer Science Issues

More than a traditional journal...

Feature Selection is a preprocessing technique in supervised learning for improving predictive accuracy while reducing dimension in clustering and categorization. Multitype Feature Coselection for Clustering (MFCC) with hard k-means is the algorithm which uses intermediate results in one type of feature space enhancing feature selection in other spaces, better feature set is co-selected by heterogeneous features to produce better cluster in each space. Db-Scan is a density-based clustering algorithm finding a number of clusters starting from the estimated density distribution of corresponding nodes. It is one of the most common clustering algorithms and also most cited in scientific literature, as a generalization of DBSCAN to multiple ranges, effectively replacing the parameter with a maximum search radius.This paper presents the empirical results of the MFCC algorithm with Db-scan and also gives the comparison results of MFCC with hard k-means and DB-Scan. DB-Scan clustering is proposed for getting the quality clustering against the outliers and time criteria is less than any other clustering in high density data set.