A novel attribute-based dynamic clustering with schedule-based rotation method for outlier detection Online publication date: Thu, 30-Jan-2020
by G. Karthikeyan; P. Balasubramanie
International Journal of Business Intelligence and Data Mining (IJBIDM), Vol. 16, No. 2, 2020
Abstract: Detection of outliers in bank transactions has gained popularity in the recent years. The existing outlier detection techniques are unable to process the high volume of data. Hence, to address this issue, an efficient attribute-based dynamic clustering-schedule-based rotation (ADC-SBR) method is proposed. The similarity between transactions within a cluster is estimated using Jaccard coefficient-based labelling approach and the optimal cluster head is chosen by the similarity-based cluster head selection (SbCHS) method. The outlier detection is performed in two levels. The node level outlier detection is performed using linear regression model and the cluster level outlier detection is performed by deviation-based ranking. An own dataset with bank transactions is used for the experimental analysis. The suggested method is implemented in Apache Spark and is compared with existing algorithms for the metrics. The comparison results prove that the proposed method is optimal for all metrics than existing algorithms.
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