Name
Abstract | ||
|---|---|---|
Partial periodic pattern mining has recently become an important issue in the field of data mining due to its wide applications in many businesses. A partial periodic pattern considers part of but not all the events within a specific period length, repeating with high frequency in an event sequence. Traditional partial periodic pattern mining, however, only considered the frequencies of patterns, but did not consider events that might have different importance. The study thus proposes a weighted partial periodic patterns mining algorithm to resolve this problem. To increase the efficiency, the two-phase upper-bound weighted model based on segmental maximum weights is adopted to prune unimportant candidates in early stage. Then the weighted partial periodic patterns are discovered from the candidate patterns. Finally, the experimental results on synthetic datasets and a real oil dataset show that the weighted partial periodic pattern mining is more practical to assist users for decision making. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1016/j.engappai.2014.01.004 | Eng. Appl. of AI |
Keywords | Field | DocType |
different importance,mining algorithm,partial periodic pattern mining,two-phase approach,early stage,candidate pattern,traditional partial periodic pattern,partial periodic pattern,weighted partial periodic pattern,two-phase upper-bound weighted model,event sequence,event,projection,data mining | Data mining,Computer science,Algorithm,Event sequence,Two phase approach,Artificial intelligence,Period length,Data mining algorithm,Periodic graph (geometry),Machine learning | Journal |
Volume | ISSN | Citations |
30, | 0952-1976 | 7 |
PageRank | References | Authors |
0.47 | 21 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kung-Jiuan Yang | 1 | 33 | 3.48 |
| Tzung-pei Hong | 2 | 3768 | 483.06 |
| Guo-Cheng Lan | 3 | 332 | 19.45 |
| Yuh-Min Chen | 4 | 379 | 32.12 |