DENCLUE-IM: A New Approach for Big Data Clustering

Every day, a large volume of data is generated by multiple sources, social networks, mobile devices, etc. This variety of data sources produce an heterogeneous data, which are engendered in high frequency. One of the techniques allowing to a better use and exploit this kind of complex data is clustering. Finding a compromise between performance and speed response time present a major challenge to classify this monstrous data. For this purpose, we propose an efficient algorithm which is an improved version of DENCLUE, called DENCLUE-IM. The idea behind is to speed calculation by avoiding the crucial step in DENCLUE which is the Hill Climbing step. Experimental results using large datasets proves the efficiency of our proposed algorithm.