CrossClus: User-guided multi-relational clustering

Xiaoxin Yin, Jiawei Han, Philip S. Yu

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)321-348
Number of pages28
JournalData Mining and Knowledge Discovery
Volume15
Issue number3
DOIs
StatePublished - Dec 2007

Keywords

  • Clustering
  • Relational data mining

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computer Networks and Communications

Fingerprint Dive into the research topics of 'CrossClus: User-guided multi-relational clustering'. Together they form a unique fingerprint.

Cite this