A hybrid algorithm for mining local outliers in categorical data Online publication date: Fri, 13-Oct-2017
by Meiling Liu; Mingxuan Huang; Weidong Tang
International Journal of Wireless and Mobile Computing (IJWMC), Vol. 13, No. 1, 2017
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.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Wireless and Mobile Computing (IJWMC):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook