Name
Abstract | ||
|---|---|---|
Co-clustering is the simultaneous partitioning of the rows and columns of a matrix such that the blocks induced by the row/column partitions are good clusters. Motivated by several applications in text mining, market-basket analysis, and bioinformatics, this problem has attracted severe attention in the past few years. Unfortunately, to date, most of the algorithmic work on this problem has been heuristic in nature. In this work we obtain the first approximation algorithms for the co-clustering problem. Our algorithms are simple and obtain constant-factor approximation solutions to the optimum. We also show that co-clustering is NP-hard, thereby complementing our algorithmic result. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1145/1376916.1376945 | PODS |
Keywords | Field | DocType |
algorithmic result,algorithmic work,co-clustering problem,market-basket analysis,simultaneous partitioning,constant-factor approximation solution,column partition,approximation algorithm,severe attention,good cluster,approximation,co clustering,biclustering,market basket analysis,clustering,text mining | Approximation algorithm,Row and column spaces,Cluster (physics),Heuristic,Correlation clustering,Computer science,Matrix (mathematics),Theoretical computer science,Biclustering,Cluster analysis | Conference |
Citations | PageRank | References |
27 | 1.14 | 26 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Aris Anagnostopoulos | 1 | 1054 | 67.08 |
| Anirban Dasgupta | 2 | 2535 | 136.99 |
| Ravi Kumar | 3 | 13932 | 1642.48 |