Name
Abstract | ||
|---|---|---|
In this paper, we study acceleration methods for on-line stream mining of all frequent closed itemsets under a minimal-size restriction. The algorithm LC-K-CloStream [3] can perform an epsilon-approximation on-line milling based on incremental intersection of transactions. We first integrate LC-K-CloStream with an extended FP-tree with skipping in order to effectively compress a huge number of mined closed itemsets. Next, we introduce novel pruning methods for rejecting a hopeless intersection computation by using look-ahead maximal-size estimation. We show, through experimental evaluations, that the proposed methods have a great performance for mining a large set of closed itemsets in dense data sets. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/BigData47090.2019.9006573 | 2019 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA) |
Keywords | Field | DocType |
stream data mining, closed itemset, FP-tree, minimal-size condition, maximal-size estimation, epsilon-approximation | Data mining,Data set,Computer science,Acceleration,Computation,Fold (higher-order function) | Conference |
ISSN | Citations | PageRank |
2639-1589 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Koji Iwanuma | 1 | 138 | 17.65 |
| Takumi Nishina | 2 | 0 | 1.01 |
| Yoshitaka Yamamoto | 3 | 29 | 7.50 |