A hybrid algorithm for mining local outliers in categorical data. (2017)

• Record Type: Journal Article
• Authors: Liu, Meiling; Huang, Mingxuan; Tang, Weidong
• Is Part Of: International journal of wireless and mobile computing. Volume 13:Number 1(2017)
• Page Start: 78
• Page End: 85
• Publication Date: 2017
• Subjects: local outlier -- standard deviation -- local outlier factor -- clustering -- data mining; Mobile computing -- Periodicals; Wireless communication systems -- Periodicals; 004.6
• Languages: English
• ISSNs: 1741-1084

Abstract

Outlier detection is an important task in data mining. Many approaches have been developed to detect outliers. However, most researches focus on global outlier detection. In many situations, the local outlier detection is more valuable than the global outlier detection. In this paper, the existing methods for outlier detection are discussed firstly, and then the definition of local outlier and some formulas are given. Also a hybrid algorithm for mining local outlier is proposed which is based on clustering algorithm and standard deviation in statistics. By calculating the standard deviation of a cluster and local outlier factor of an object in the cluster, we can identify that the clusters with higher standard deviation may have outliers, and the objects with higher local outlier factor can be recognised as outliers. Experimental results on real datasets show that the proposed algorithm is correct and effective for mining local outliers.