Name
Abstract | ||
|---|---|---|
When a mining procedure is directly executed on very large databases, the computer memory may not allow the processing in memory. In the past, we adopted a branch-and-bound search strategy to divide the domain items as a set of groups. Although it works well in partitions the items, the time is quite time consuming. In this paper, we thus propose a GA-based approach to speed up the partition process. A new encoding representation and a transformation scheme are designed to help the search process. Experimental results also show that the algorithm can get a proper partition with good efficiency. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ICSMC.2011.6084010 | SMC |
Keywords | Field | DocType |
ga-based item partition,insert,styling,style,tree searching,transformation scheme,genetic algorithms,branch-and-bound search strategy,data mining,very large databases,formatting,encoding representation,association rules | Data mining,Computer science,Theoretical computer science,Association rule learning,Artificial intelligence,Disk formatting,Partition (number theory),Computer memory,Machine learning,Genetic algorithm,Speedup,Encoding (memory) | Conference |
ISSN | ISBN | Citations |
1062-922X | 978-1-4577-0652-3 | 0 |
PageRank | References | Authors |
0.34 | 7 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tzung-pei Hong | 1 | 3768 | 483.06 |
| Jheng-Nan Huang | 2 | 1 | 1.03 |
| Wen-Yang Lin | 3 | 399 | 35.72 |
| Ming-Chao Chiang | 4 | 375 | 44.19 |