Name
Abstract | ||
|---|---|---|
High utility mining is a fundamental topic in association rule mining, which aims to discover all itemsets with high utility from transaction database. The previous studies are mainly based on fixed databases, which are not applicable for incremental databases. Although incremental high utility pattern (IHUP) mining has been proposed, its tree structure IHUP-Tree is redundant and thus IHUP algorithm has relative low efficiency. To address this issue, we propose an incremental compressed high utility mining algorithm called iCHUM. The iCHUM algorithm utilizes items of high transaction weighted utilization (TWU) to construct its tree structure, namely iCHUM-Tree. The iCHUM algorithm updates iCHUM-Tree when new database is appended to the original database. The information of high utility itemsets is maintained in the iCHUM-Tree such that candidate itemsets can be generated through mining procedure. Performance analysis shows that our algorithm is more efficient than baseline approaches in incremental databases. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-25159-2_20 | KSEM |
Keywords | Field | DocType |
Data mining,Association rule,High utility mining,Incremental mining | Utility mining,Data mining,GSP Algorithm,Computer science,Algorithm,FSA-Red Algorithm,Association rule learning,Tree structure,Artificial intelligence,Database transaction,Database,Machine learning | Conference |
Volume | ISSN | Citations |
9403 | 0302-9743 | 1 |
PageRank | References | Authors |
0.35 | 14 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zheng Hai-Tao | 1 | 142 | 24.39 |
| Zhuo Li | 2 | 187 | 37.36 |