Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6948391 | Decision Support Systems | 2018 | 14 Pages |
Abstract
Detecting changes of behaviors or events is crucial when updating existing knowledge in a dynamic business environment. Currently, data analysts can immediately collect data and easily access existing knowledge. However, that knowledge can also rapidly become outdated. This study discusses a form of knowledge, classifiable sequential patterns (CSPs), defined as s â c, where s is a temporal sequence; c is a class label; and “â” is a sign which implies the sequential relationships between s (cause) and c (effect). If the CSP evolves into another, and the new knowledge is not updated, decision-makers would continue to work with the obsolete CSP. To the authors' knowledge, no study has addressed the topic of change mining in CSPs. To address this research gap, this study proposes a novel change-mining model, SeqClassChange, to identify changes in CSPs. Experiments were conducted with a real-world dataset to evaluate the proposed model.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Information Systems
Authors
Tony Cheng-Kui Huang, Pu-Tai Yang, Jen-Hung Teng,