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| Content Provider | ACM Digital Library |
|---|---|
| Author | Alqadah, Faris Bhatnagar, Raj |
| Abstract | Conventional clustering algorithms group similar data points together along one dimension of a data table. Bi-clustering simultaneously clusters both dimensions of a data table. 3-clustering goes one step further and aims to concurrently cluster two data tables that share a common set of row labels, but whose column labels are distinct. Such clusters reveal the underlying connections between the elements of all three sets. We present a novel algorithm that discovers 3-clusters across vertically partitioned data. Our approach presents two new and important formulations: first we introduce the notion of a 3-cluster in partitioned data; and second we present a mathematical formulation that measures the quality of such clusters. Our algorithm discovers high quality, arbitrarily positioned, overlapping clusters, and is efficient in time. These results are exhibited in a comprehensive study on real datasets. |
| Starting Page | 1103 |
| Ending Page | 1112 |
| Page Count | 10 |
| File Format | |
| ISBN | 9781595939913 |
| DOI | 10.1145/1458082.1458228 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2008-10-26 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Formal concept analysis Bi-clustering Multi-clustering |
| Content Type | Text |
| Resource Type | Article |
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