CrossClus: user-guided multi-relational clustering |
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Authors: | Xiaoxin Yin Jiawei Han Philip S Yu |
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Affiliation: | (1) Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA;(2) IBM T.J. Watson Research Center, Yorktown Heights, NY, USA |
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Abstract: | Most structured data in real-life applications are stored in relational databases containing multiple semantically linked
relations. Unlike clustering in a single table, when clustering objects in relational databases there are usually a large
number of features conveying very different semantic information, and using all features indiscriminately is unlikely to generate
meaningful results. Because the user knows her goal of clustering, we propose a new approach called CrossClus, which performs multi-relational clustering under user’s guidance. Unlike semi-supervised clustering which requires the user
to provide a training set, we minimize the user’s effort by using a very simple form of user guidance. The user is only required
to select one or a small set of features that are pertinent to the clustering goal, and CrossClus searches for other pertinent features in multiple relations. Each feature is evaluated by whether it clusters objects in
a similar way with the user specified features. We design efficient and accurate approaches for both feature selection and
object clustering. Our comprehensive experiments demonstrate the effectiveness and scalability of CrossClus.
The work was supported in part by the U.S. National Science Foundation NSF IIS-03-13678 and NSF BDI-05-15813, and an IBM Faculty
Award. Any opinions, findings, and conclusions or recommendations expressed in this paper are those of the authors and do
not necessarily reflect views of the funding agencies. |
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Keywords: | Relational data mining Clustering |
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