NDLI: Mining Interestingness Sub-cubes in Multi-dimensional Data

Content Provider	IEEE Xplore Digital Library
Author	Xiting Li Xiuli Ma Shiwei Tang Dongqing Yang
Copyright Year	2008
Description	Author affiliation: Sch. of Electron. Eng. & Comput. Sci., Peking Univ., Beijing (Xiting Li; Xiuli Ma; Shiwei Tang; Dongqing Yang)
Abstract	When dealing with the multi-dimensional data, despite all the data cells presented, users may only be interested in those that satisfy some condition. Now that the desired cells are scattered around, our goal is to identify the sub-cubes with high proportion of desired cells, which shed light on the distributions that users are most interested in, which we denote as interestingness sub-cubes. With all of the cells satisfying the given condition, the cube is called closed interestingness sub-cube (CIS). With a few undesired cells being involved, the cube turns flowed interestingness sub-cube (FIS) which may represent users' interest more generally and accurately. In this paper, we study the problem of CIS mining and FIS mining. For CIS mining, a 3D frequent closed cube mining algorithm is extended to suit multi-dimensional circumstance. For FIS mining, a bottom-up and top- down combined method is proposed to rapidly locate the possible interestingness sub-cubes followed by heuristic cutting. Our performance study shows that both are effective and efficient.
Starting Page	401
Ending Page	405
File Size	707687
Page Count	5
File Format	PDF
ISBN	9780769533056
DOI	10.1109/FSKD.2008.530
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-10-18
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Knowledge engineering Multidimensional systems Closed interestingness sub-cube mining Light scattering FCC Data engineering OLAP Computational Intelligence Society Data mining Computer science Multi-dimensional data Clustering algorithms Fuzzy systems
Content Type	Text
Resource Type	Article

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