NDLI: AORS: Affinity-based outlier ranking score

Content Provider	IEEE Xplore Digital Library
Author	Shaohong Zhang Hau-San Wong Wen-Jun Shen Dongqing Xie
Copyright Year	2014
Description	Author affiliation: Dept. of Comput. Sci., Guangzhou Univ., Guangzhou, China (Shaohong Zhang; Dongqing Xie) \|\| Dept. of Comput. Sci., City Univ. of Hong Kong, Hong Kong, China (Hau-San Wong; Wen-Jun Shen)
Abstract	Outlier ranking methods can provide a quantitative measure to evaluate the outlierness of data instances in data clustering and attract great interest in pattern recognition and data mining communities. However, it has been pointed out that the diverse scaling ranges of these scores bring difficulty to result interpretation. Moreover, popular outlier ranking scores based on simple distance measures might not accurately reflect the complex affinity among data points. In this paper, we propose a new outlier ranking method based on consensus affinity of a cluster ensemble. Two new outlier ranking scores generalized from well-known clustering evaluation measures, Rvv from the RAND measure and ARIvv from Adjusted Rand Index (ARI), are adopted for outlierness evaluation. Compared to other outlierness ranking measures, the two new measures have the desired bounds without additional transformations. Consistent with the improvement of Adjusted Rand Index (ARI) over RAND, we find that ARIvv also significantly outperforms Rvv. Benefiting from the consensus affinity of a cluster ensemble, our proposed method with the ARIvv score provides significant improvement beyond a number of competing algorithms on public UCI benchmark data sets. Studies with both theoretical analysis and experimental validation show the effectiveness of our proposed methods.
Starting Page	1020
Ending Page	1027
File Size	579071
Page Count	8
File Format	PDF
ISSN	21614407
e-ISBN	9781479914845
DOI	10.1109/IJCNN.2014.6889551
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Clustering algorithms Indexes Partitioning algorithms Uncertainty Power measurement Correlation Educational institutions
Content Type	Text
Resource Type	Article

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