An Adaptive Density Data Stream Clustering Algorithm

Abstract

Now we are in the age of big data. Huge amount of data and information are generated every time. Traditional data stream algorithms are suit for the data streams with low dimension and simple structure. However, with the development of information technology, the produced data streams are becoming more and more complicated. It is particularly important to study how to find new associations and patterns from complex data to achieve the cognition ability and judgment ability like human brain. Clustering data streams with mixed attributes of irregular distribution is a big challenge in data mining. To solve this problem, we present an adaptive density data stream clustering algorithm—ADStream. ADStream is based on the online–off-line clustering framework. It can automatically recognize the initial clusters by passing messages between data points. Then a novel time-decay density clustering strategy is designed to group and update the continuously arriving data streams. Comprehensive experimental results demonstrate that ADStream is adaptive to the evolving data streams and may generate high-quality clusters with fast processing rate.