TY - JOUR
T1 - CrossClus
T2 - User-guided multi-relational clustering
AU - Yin, Xiaoxin
AU - Han, Jiawei
AU - Yu, Philip S.
N1 - Funding Information:
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.
PY - 2007/12
Y1 - 2007/12
N2 - 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.
AB - 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.
KW - Clustering
KW - Relational data mining
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U2 - 10.1007/s10618-007-0072-z
DO - 10.1007/s10618-007-0072-z
M3 - Article
AN - SCOPUS:35548932004
VL - 15
SP - 321
EP - 348
JO - Data Mining and Knowledge Discovery
JF - Data Mining and Knowledge Discovery
SN - 1384-5810
IS - 3
ER -