Name
Abstract | ||
|---|---|---|
Sequential pattern mining is a crucial but challenging task in many applications, e.g., analyzing the behaviors of data in transactions and discovering frequent patterns in time series data. This task becomes difficult when valuable patterns are locally or implicitly involved in noisy data. In this paper, we propose a method for mining such local patterns from sequences. Using rough set theory, we describe an algorithm for generating decision rules that take into account local patterns for arriving at a particular decision. To apply sequential data to rough set theory, the size of local patterns is specified, allowing a set of sequences to be transformed into a sequential information system. We use the discernibility of decision classes to establish evaluation criteria for the decision rules in the sequential information system. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1016/j.ijar.2011.03.002 | Int. J. Approx. Reasoning |
Keywords | Field | DocType |
rough set theory,decision rule,decision class,local pattern,sequential pattern mining,local patterns,noisy data,time series data,sequential information system,sequential pattern mining algorithm,sequential data,particular decision,information system | Information system,Transaction processing,Data mining,Time series,Computer science,Artificial intelligence,Dominance-based rough set approach,Decision rule,Signal-to-noise ratio,Algorithm,Rough set,Knowledge extraction,Machine learning | Journal |
Volume | Issue | ISSN |
52 | 6 | International Journal of Approximate Reasoning |
Citations | PageRank | References |
27 | 0.87 | 27 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ken Kaneiwa | 1 | 270 | 24.26 |
| Yasuo Kudo | 2 | 95 | 26.41 |