Name
Abstract | ||
|---|---|---|
With the existence of many large transaction databases, the huge amounts of data, the high scability of distributed systems, and the easy partition and distribution of a centralized database, it is important to investigate efficient methods for distributed mining of association rules. This study discloses some interesting relationships between locally large and globally large itemsets and proposes an interesting distributed association rule mining algorithm, FDM (Fast Distributed Mining of association rules), which generates a small number of candidate sets and substantially reduces the number of messages to be passed at mining association rules. Our performance study shows that FDM has a superior performance over the direct application of a typical sequential algorithm. Further performance enhancement leads to a few variations of the algorithm. |
Year | DOI | Venue |
|---|---|---|
1996 | 10.1109/PDIS.1996.568665 | PDIS |
Keywords | Field | DocType |
association rule,data mining,distributed algorithm,computer science,scalability,distributed algorithms,relational databases,association rules,transaction processing,association rule mining,distributed databases,distributed systems,distributed system | Transaction processing,Data mining,Computer science,Apriori algorithm,Theoretical computer science,FSA-Red Algorithm,Distributed algorithm,Association rule learning,Distributed database,Sequential algorithm,Scalability | Conference |
ISBN | Citations | PageRank |
0-8186-7475-X | 169 | 13.68 |
References | Authors | |
12 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| David Wai-Lok Cheung | 1 | 2469 | 282.09 |
| Jiawei Han | 2 | 43085 | 3824.48 |
| Vincent T. Y. Ng | 3 | 504 | 122.85 |
| Ada Wai-Chee Fu | 4 | 4646 | 417.59 |
| Yongjian Fu | 5 | 1385 | 408.32 |