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

Outlier detectionin streamingdataisavery challenging problem. Thisis becauseofthefactthatdata streams cannotbe scanned multiple times. Alsonew conceptsmaykeepevolving. Irrelevant attributescanbe termedasnoisy attributesandsuch attributes further magnify the challenge of working with data streams. In this paper, we propose a clustering based framework for outlier detectioninevolving data streams that assigns weightsto attributes depending upon their respective relevance.Weighted attributes arehelpfultoreduceorremovetheeffectofnoisyattributesinminingtasks.Keepinginviewthe challengesofdatastreammining, 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 otherexisting approachesin termsof outlier detection rate,false alarm rate, running time and with increasing percentages of outliers.