Interactive Clustering for Transaction Data

We propose a clustering algorithm, OAK, targeted to transaction data as typified by market basket data, web documents, and categorical data. OAK is interactive, incremental, and scalable. Use of a dendrogram facilitates the dynamic modification of the number of clusters. In addition, a condensation...

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Bibliographische Detailangaben
Hauptverfasser: Xiao, Yongqiao, Dunham, Margaret H.
Format: Tagungsbericht
Sprache:eng
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Beschreibung
Zusammenfassung:We propose a clustering algorithm, OAK, targeted to transaction data as typified by market basket data, web documents, and categorical data. OAK is interactive, incremental, and scalable. Use of a dendrogram facilitates the dynamic modification of the number of clusters. In addition, a condensation technique ensures that the dendrogram (regardless of database size) can be memory resident. A performance study shows that the quality of clusters is comparable to ROCK [7] with reduced complexity.
ISSN:0302-9743
1611-3349
DOI:10.1007/3-540-44801-2_13