Discovery of Non-Interesting Attribute in Mining Outliers Pattern

Faizah Shaari, and Azuraliza, A.B. and Abdul H. Razak, (2007) Discovery of Non-Interesting Attribute in Mining Outliers Pattern. In: International Conference Computational Science and its Applications (ICCSA 2007). IEEE Computer Society, 26-29 August 2007, Kuala Lumpur, Malaysia.

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Official URL: http://csdl2.computer.org/persagen/DLAbsToc.jsp?resourcePath=/dl/proceedings/iccsa/&toc=comp/proceedings/iccsa/2007/2945/00/2945toc.xml&DOI=10.1109/ICCSA.2007.31

Affiliations

Universiti Kebangsaan Malaysia

Abstract

An outlier in a dataset is a point or a class of points that is considerably dissimilar to or inconsistent with the remainder of the data. Detection of outliers is important for many applications and has always attracted attention among data mining research community. In this paper, we present a new method in detecting outlier by discovering Non-IntAttrb from the information system (IS). Non-IntAttrb is set of attributes from IS that may contain outliers. We discover the computation of Non-IntAttrb by defining indiscernibility matrix modulo (iDMM) and indiscernibility function modulo(iDFM). We define a measurement called RSetOF(Rough Set Outlier Factor Value) to detect outlier objects. The experimental results show that our approach is a fast outlier detection method.

Item Type:	Conference or Workshop Item (Paper)
Keywords:	Information system; data mining;
Subjects:	Q Science
ID Code:	1150

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