An Efficient Approach Generating Optimized Clusters for Theoretic Clustering Using Data Mining

The aim of the data mining process is to extract information from a large data set and transform it into an understandable structure for further use. Data mining is the process of finding anomalies, patterns and correlations within large data sets to predict outcomes. Using a broad range of techniques, you can use this information to increase revenues, cut costs, improve customer relationships, and reduce risks and more. Clustering is a main task of exploratory data analysis and data mining applications. Theoretic Clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). Centralizing the whole data at one source is not possible and it is impractical. Thus, there is great demand for distributed data clustering algorithms. Distributed clustering is to explore the hidden structure of the data collected/stored in geographically distributed nodes. Information theoretic measures take the whole distribution of cluster data into account for optimized clustering results. For this, we incorporate an information theoretic measure into the cost function of the distributed clustering. The proposed Diffused Kernel DMMI algorithm with association rule mining can achieve optimized cluster results on textual and numeric data in less time. Keywords— ―Data mining‖, ―Theoretic clustering‖, ―clustering‖, ―Mutual information‖, ―Diffusion‖