A Framework for Outlier Detection in Evolving Data Streams by Weighting Attributes in Clustering

Outlier detection in streaming data is a very challenging problem. This is because of the fact that data streams cannot be scanned multiple times. Also new concepts may keep evolving. Irrelevant attributes can be termed as noisy attributes and such attributes further magnify the challenge of working with data streams. In this paper, we propose a clustering based framework for outlier detection in evolving data streams that assigns weights to attributes depending upon their respective relevance. Weighted attributes are helpful to reduce or remove the effect of noisy attributes in mining tasks. Keeping in view the challenges of data stream mining, the proposed framework is incremental and adaptive to concept evolution. Experimental results on synthetic and real world data sets show that our proposed approach outperforms other existing approaches in terms of outlier detection rate, false alarm rate, running time and with increasing percentages of outliers. (C) 2012 The Authors. Published by Elsevier Ltd. Selection and/or peer-review under responsibility of the Department of Computer Science & Engineering, National Institute of Technology Rourkela