Name
Abstract | ||
|---|---|---|
Association mining techniques search for groups of frequently co-occurring items in a market-basket type of data and turn these groups into business-oriented rules. Previous research has focused predominantly on how to obtain exhaustive lists of such associations. However, users often prefer a quick response to targeted queries. For instance, they may want to learn about the buying habits of customers that frequently purchase cereals and fruits. To expedite the processing of such queries, we propose an approach that converts the market-basket database into an itemset tree. Experiments indicate that the targeted queries are answered in a time that is roughly linear in the number of market baskets, N. Also, the construction of the itemset tree has O(N) space and time requirements. Some useful theoretical properties are proven. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1109/TKDE.2003.1245290 | IEEE Trans. Knowl. Data Eng. |
Keywords | Field | DocType |
indexing terms,tree data structures,computational complexity,data mining | Data mining,Computer science,Tree (data structure),Association mining,Artificial intelligence,Machine learning,Computational complexity theory | Journal |
Volume | Issue | ISSN |
15 | 6 | 1041-4347 |
Citations | PageRank | References |
28 | 0.99 | 22 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Miroslav Kubat | 1 | 2384 | 231.57 |
| Alaaeldin Hafez | 2 | 49 | 3.77 |
| Vijay V. Raghavan | 3 | 2544 | 506.92 |
| Jayakrishna R. Lekkala | 4 | 28 | 0.99 |
| Wei Kian Chen | 5 | 29 | 1.70 |