Adaptation in clustering algorithm by algorithm output granularity for mobile data stream mining

This paper presents an overview of the current state-of-the-art in mobile data stream mining and its applications. The paper presents the strategies and techniques for adaptation that are essential in order to perform real-time, continuous data mining on mobile devices. We present an overview of adaptation strategies for data stream mining and in particular for memory conservation with Algorithm Output Granularity. For mining purpose, we uses k-means clustering algorithm.