Differential Evolution based Cluster optimization for Multi valued data sets

Abstract

In data analysis, items were mostly described by a set of characteristics called features,in which each feature contains only single value for each object. Even so, in existence, some features may include more than one value, such as a person with different job descriptions, activities, phone numbers, skills and different mailing addresses. Such features may be called as multi-valued features, and are mostly classified as null features while analyzing the data using machine learning and data mining techniques.  In this paper, a proximity function is described to find the proximity between two substances with multi-valued features that are put into effect for Clustering. This distance measure approach allows iterative measurements of the similarities around objects as well as their characteristics. For facilitating the most suitable multi-valued factors, we put forward a model targeting at determining each factor’s relative prominence for diverse data extracting problems. The proposed model is an evolutionary strategy that uses Differential strategy for evolutions, which is using the degree of member ship as fitness function. The proposed clustering algorithm as multi valued attribute data cluster optimization based on the strategy of Differential evolution (MVA-DE). Therefore this becomes feasible using any mechanisms for cluster analysis to group similar data. The effectiveness of our model is evinced by performance analysis carried through experimental study. The outcomes of the experiments carried on proposed model were compared with other strategic clustering approaches like fuzzy c- means based Clustering of Multivalued Attribute Data (FCM-MVA) and K-Means with Tanimoto based multi-valued data clustering. The findings demonstrate that our test not only improves the performance the traditional measure of similarity but also outperforms other clustering algorithms on the multi-valued clustering framework.