A novel rough set based clustering approach for streaming data

Abstract

Clustering is a very important data mining task. Clustering of streaming data is very challenging because streaming data cannot be scanned multiple times and also new concepts may keep evolving in data over time. Inherent uncertainty involved in real world data stream further magnifies the challenge of working with streaming data. Rough set is a soft computing technique which can be used to deal with uncertainty involved in cluster analysis. In this paper, we propose a novel rough set based clustering method for streaming data. It describes a cluster as a pair of lower approximation and an upper approximation. Lower approximation comprises of the data objects that can be assigned with certainty to the respective cluster, whereas upper approximation contains those data objects whose belongingness to the various clusters in not crisp along with the elements of lower approximation. Uncertainty in assigning a data object to a cluster is captured by allowing overlapping in upper approximation. Proposed method generates soft-cluster. Keeping in view the challenges of streaming data, the proposed method is incremental and adaptive to evolving concept. Experimental results on synthetic and real world data sets show that our proposed approach outperforms Leader clustering algorithm in terms of classification accuracy. Proposed method generates more natural clusters as compare to k-means clustering and it is robust to outliers. Performance of proposed method is also analyzed in terms of correctness and accuracy of rough clustering.