A New Evolving Data Streams System with Data Fusion

Cluster analysis is an important data mining issue, where objects under investigation are grouped into subsets of the original set of objects. In recent several years, a few clustering algorithms have been developed for the data stream problem. However these algorithms lack of extensibility or efficiency. In this paper we propose a new evolving data streams system with data fusion. We discuss a fundamentally different philosophy for data stream clustering which is guided by application centered requirements. The system is highly suitable for real-time implementation and is demonstrated through a series of experiments. The experiments over a number of real and synthetic data sets illustrate the effectiveness and efficiency.