Name
Title | ||
|---|---|---|
ApproxMGMSP: A Scalable Method of Mining Approximate Multidimensional Sequential Patterns on Distributed System |
Abstract | ||
|---|---|---|
We present a scalable and effective algorithm called ApproxMGMSP (Approximate Mining of Global Multidimensional Sequential Patterns) to solve the problem of mining the multidimensional sequential patterns for large databases in the distributed environment. Our method differs from previous related works of mining multidimensional patterns on distributed system. The main difference is that an approximate mining method is used in large multidimensional sequence database firstly. In this paper, to convert the mining on the multidimensional sequential patterns to sequential patterns, the multidimensional information is embedded into the corresponding sequences. Then the sequences are clustered, summarized, and analyzed on the distributed sites, and the local patterns could be obtained by the effective approximate sequential pattern mining method. Finally, the global multidimensional sequential patterns could be quickly mined by high vote sequential pattern model after collecting all the local patterns on one site. Both the theories and the experiments indicate that this method could simplify the problem of mining the multidimensional sequential patterns and avoid mining the redundant information. The global sequential patterns could be obtained effectively by the scalable method after reducing the cost of communication. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/FSKD.2007.192 | FSKD (2) |
Keywords | Field | DocType |
large multidimensional sequence database,multidimensional sequence database,approximate multidimensional sequential patterns,scalable method,mining method,global sequential pattern,high vote sequential pattern,effective approximate sequential pattern,mining multidimensional pattern,multidimensional information,approxmgmsp,mining approximate multidimensional sequential,multidimensional sequential pattern,data mining,global multidimensional sequential patterns,approximate mining method,local pattern,global multidimensional sequential pattern,approximate sequential pattern mining,distributed system,large databases,distributed processing,approximate mining,sequential pattern mining,distributed environment | Data mining,Sequence database,Distributed Computing Environment,Computer science,Sequential Pattern Mining,Distributed computing,Scalability | Conference |
Volume | ISBN | Citations |
2 | 978-0-7695-2874-8 | 7 |
PageRank | References | Authors |
0.50 | 9 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Changhai Zhang | 1 | 109 | 8.37 |
| Kongfa Hu | 2 | 38 | 9.26 |
| Zhuxi Chen | 3 | 8 | 1.24 |
| Ling Chen | 4 | 42 | 6.49 |
| Yisheng Dong | 5 | 245 | 20.54 |