Algorithm for mining outliers in categorical data through ranking

Ranga Suri, NNR and Murty, Narasimha M and Athithan, G (2012) Algorithm for mining outliers in categorical data through ranking. In: 2012 12th International Conference on Hybrid Intelligent Systems (HIS), 4-7 Dec. 2012, Pune, India.

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Abstract

The rapid growth in the field of data mining has lead to the development of various methods for outlier detection. Though detection of outliers has been well explored in the context of numerical data, dealing with categorical data is still evolving. In this paper, we propose a two-phase algorithm for detecting outliers in categorical data based on a novel definition of outliers. In the first phase, this algorithm explores a clustering of the given data, followed by the ranking phase for determining the set of most likely outliers. The proposed algorithm is expected to perform better as it can identify different types of outliers, employing two independent ranking schemes based on the attribute value frequencies and the inherent clustering structure in the given data. Unlike some existing methods, the computational complexity of this algorithm is not affected by the number of outliers to be detected. The efficacy of this algorithm is demonstrated through experiments on various public domain categorical data sets.

Item Type:	Conference Paper
Publication:	2012 12th International Conference on Hybrid Intelligent Systems (HIS)
Publisher:	IEEE
Additional Information:	Copyright of this article belongs to IEEE.
Keywords:	Data Mining; Outlierdetection; Categorical Data; Data Clustering
Department/Centre:	Division of Electrical Sciences > Computer Science & Automation
Date Deposited:	02 Jul 2013 08:34
Last Modified:	02 Jul 2013 08:34
URI:	http://eprints.iisc.ac.in/id/eprint/46625

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