Name
Abstract | ||
|---|---|---|
On-shelf utility mining has recently received interest in the data mining field due to its practical considerations. On-shelf utility mining considers not only profits and quantities of items in transactions but also their on-shelf time periods in stores. Profit values of items in traditional on-shelf utility mining are considered as being positive. However, in real-world applications, items may be associated with negative profit values. This paper proposes an efficient three-scan mining approach to efficiently find high on-shelf utility itemsets with negative profit values from temporal databases. In particular, an effective itemset generation method is developed to avoid generating a large number of redundant candidates and to effectively reduce the number of data scans in mining. Experimental results for several synthetic and real datasets show that the proposed approach has good performance in pruning effectiveness and execution efficiency. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1016/j.eswa.2013.10.049 | Expert Syst. Appl. |
Keywords | Field | DocType |
traditional on-shelf utility mining,data mining field,negative profit value,negative item value,on-shelf utility mining,high on-shelf utility itemsets,profit value,large number,efficient three-scan mining approach,on-shelf time period,data mining | Utility mining,Data mining,Computer science,Temporal database,Artificial intelligence,Machine learning,Profit (economics) | Journal |
Volume | Issue | ISSN |
41 | 7 | 0957-4174 |
Citations | PageRank | References |
19 | 0.65 | 20 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guo-Cheng Lan | 1 | 332 | 19.45 |
| Tzung-pei Hong | 2 | 3768 | 483.06 |
| Jen-Peng Huang | 3 | 57 | 6.45 |
| Vincent S. Tseng | 4 | 2923 | 161.33 |