Name
Abstract | ||
|---|---|---|
Structural relation patterns have been introduced recently to extend the search for complex patterns often hidden behind large sequences of data. This has motivated a novel approach to sequential patterns post-processing and a corresponding data mining method was proposed for Concurrent Sequential Patterns ConSP. This article refines the approach in the context of ConSP modelling, where a companion graph-based model is devised as an extension of previous work. Two new modelling methods are presented here together with a construction algorithm, to complete the transformation of concurrent sequential patterns to a ConSP-Graph representation. Customer orders data is used to demonstrate the effectiveness of ConSP mining while synthetic sample data highlights the strength of the modelling technique, illuminating the theories developed. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.4018/jdwm.2010040103 | IJDWM |
Keywords | Field | DocType |
consp modelling,novel approach,new modelling method,corresponding data mining method,concurrent sequential pattern,customer orders data,concurrent sequential patterns consp,synthetic sample data,modelling technique,consp mining,graph-based modelling,computing | Graph,Data mining,Computer science,Artificial intelligence,Machine learning | Journal |
Volume | Issue | ISSN |
6 | 2 | 1548-3924 |
Citations | PageRank | References |
2 | 0.44 | 16 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jing Lu | 1 | 2 | 0.44 |
| Weiru Chen | 2 | 46 | 6.64 |
| Malcolm Keech | 3 | 38 | 5.31 |