Abstract
We now present an algorithm, called PRE (parallel algorithm using database reduction technique for mining frequent episodes) by us, that is more efficient than the existing WINEPI algorithm because: (1) PRE utilizes the efficiency of parallel computing and (2) the size of database can be gradually reduced during mining. In the full paper, we explain PRE algorithm in detail; in this abstract, we just add some pertinent remarks to listing the three topics of explanation: (1) the important properties in parallel mining episodes of frequent occurrence; (2) the database reduction techniques in parallel mining frequent episodes; and (3) the iterative procedure of PRE algorithm; in topic 1, we give Theorems 1, 2, and 3 in the full paper that make clear the relations among global frequent episodes and local frequent episodes under various conditions; in topic 2, we give Theorems 4 and 5 in the full paper for reducing the database gradually during mining; in topic 3, we give a four-step iterative procedure. Finally we give some numerical examples, whose results, shown in Figs.1, 2, and 3 in the full paper, show preliminarily that, by using the database reduction techniques alone, algorithm PRE is faster than WINEPI about 25%. The experiment results also show that algorithm DRE has good speedup performance.
Original language | English |
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Pages (from-to) | 173-176 |
Number of pages | 4 |
Journal | Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University |
Volume | 25 |
Issue number | 2 |
State | Published - Apr 2007 |
Keywords
- Data mining
- Frequent episode
- Parallel algorithm