Name
Abstract | ||
|---|---|---|
We address the problem of mining targeted association rules over multidimensional market-basket data. Here, each transaction has, in addition to the set of purchased items, ancillary dimension attributes associated with it. Based on these dimensions, transactions can be visualized as distributed over cells of an n-dimensional cube. In this framework, a targeted association rule is of the form {X - Y}R, where R is a convex region in the cube and X - Y is a traditional association rule within region R. We first describe the TOARM algorithm, based on classical techniques, for identifying targeted association rules. Then, we discuss the concepts of bottom-up aggregation and cubing, leading to the Cell Union technique. This approach is further extended, using notions of cube-count interleaving and credit-based pruning, to derive the Ice Cube algorithm. Our experiments demonstrate that Ice Cube consistently provides the best execution time performance, especially for large and complex data cubes. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ICDM.2012.67 | ICDM |
Keywords | Field | DocType |
convex region,ice cube,ice cube algorithm,multidimensional market-basket data,targeted association rule,data cubes,toarm algorithm,traditional association rule,n-dimensional cube,association rule,efficient targeted mining,complex data cube,data mining | Data mining,Of the form,Computer science,Pocket Cube,Complex data type,Regular polygon,Association rule learning,Artificial intelligence,Interleaving,Data cube,Machine learning,Cube | Conference |
ISSN | Citations | PageRank |
1550-4786 | 1 | 0.36 |
References | Authors | |
5 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shrutendra K. Harsola | 1 | 1 | 0.36 |
| Prasad M. Deshpande | 2 | 1145 | 197.03 |
| Jayant R. Haritsa | 3 | 2004 | 228.38 |