Name
Abstract | ||
|---|---|---|
The real transactional databases often exhibit temporal characteristic and time varying behavior. Temporal association rule has thus become an active area of research. A calendar unit such as months and days, clock units such as hours and seconds and specialized units such as business days and academic years, play a major role in a wide range of information system applications. The calendar-based pattern has already been proposed by researchers to restrict the time-based associationships. This paper proposes a novel algorithm to find association rule on time dependent data using efficient T tree and P-tree data structures. The algorithm elaborates the significant advantage in terms of time and memory while incorporating time dimension. Our approach of scanning based on time-intervals yields smaller dataset for a given valid interval thus reducing the processing time. This approach is implemented on a synthetic dataset and result shows that temporal TFP tree gives better performance over a TFP tree approach. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11510888_64 | MLDM |
Keywords | Field | DocType |
tfp tree approach,temporal tfp tree,processing time,association rule mining,temporal characteristic,association rule,p-tree data structure,time dependent data,time varying behavior,time dimension,temporal association rule,temporal approach,data structure,information system | Transaction processing,Information system,Data structure,Computer science,T-tree,Tree (data structure),Algorithm,Temporal database,Association rule learning,Multiple time dimensions | Conference |
Volume | ISSN | ISBN |
3587 | 0302-9743 | 3-540-26923-1 |
Citations | PageRank | References |
10 | 0.52 | 12 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Keshri Verma | 1 | 28 | 1.41 |
| O. P. Vyas | 2 | 121 | 14.28 |
| Ranjana Vyas | 3 | 12 | 2.25 |