Change detection model for sequential cause-and-effect relationships
| Autor: | Pu-Tai Yang, Jen-Hung Teng, Tony Cheng-Kui Huang |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Sequence
Class (computer programming) Information Systems and Management business.industry Computer science Sign (semiotics) 02 engineering and technology Machine learning computer.software_genre Management Information Systems Arts and Humanities (miscellaneous) 020204 information systems 0202 electrical engineering electronic engineering information engineering Developmental and Educational Psychology 020201 artificial intelligence & image processing Artificial intelligence business computer Change detection Information Systems |
| Zdroj: | Decision Support Systems. 106:30-43 |
| ISSN: | 0167-9236 |
| DOI: | 10.1016/j.dss.2017.11.007 |
| Popis: | 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 However, that knowledge can also rapidly become outdated.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 ( CSP s), defined as s → c , where s is a temporal sequence; c is a class label; and “→” 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. |
| Databáze: | OpenAIRE |
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