NDLI: The Outliers Mining Algorithm Based on Constrained Concept Lattice

Content Provider	IEEE Xplore Digital Library
Author	Jiang Yiyong Zhang Jifu Cai Jianghui Zhang Sulan Hu Lihua
Copyright Year	2007
Description	Author affiliation: TaiYuan Univ. of Sci. & Technol., Taiyuan (Jiang Yiyong; Zhang Jifu; Cai Jianghui; Zhang Sulan; Hu Lihua)
Abstract	Traditional outlier mining methods regard outliers from overall point, and hardly find bias data in low dimensional subspace. Constrained concept lattice, with characteristics of higher constructing efficiency , practicability and pertinency, is a new concept lattice structure. Outlier mining algorithm RCLOM based on constrained concept lattice is presented for bias data in low dimensional subspace. The intension of constrained concept lattice nodes is regarded as subspace and sparsity coefficient is computed for every intension reductions of the nodes. If sparsity coefficient of k dimensional intension reduction is less than the sparsity coefficient threshold value, k-1 dimensional intensions are judged whether it is dense subspace. If all subspaces are dense, objects contained in the intension are seen as bias data or outliers in k dimensional subspace. In the end, the algorithm is feasible and effective for mining outliers in low dimensional subspace by the example.
Starting Page	80
Ending Page	85
File Size	350751
Page Count	6
File Format	PDF
ISBN	9780769530161
DOI	10.1109/ISDPE.2007.44
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-11-01
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computer science Data privacy Databases Subspace constraints Lattices Information retrieval Credit cards Data mining Association rules Genetic algorithms
Content Type	Text
Resource Type	Article

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