A System for Predictive Data Analytics Using Sequential Rule Mining
| Autor: | Sandipkumar Chandrakant Sagare, S.K. Shirgave, Dattatraya Vishnu Kodavade |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Computer Networks and Communications
Computer science 02 engineering and technology computer.software_genre Computer Graphics and Computer-Aided Design Computer Science Applications Artificial Intelligence 020204 information systems 0202 electrical engineering electronic engineering information engineering Data analysis 020201 artificial intelligence & image processing Data mining computer Software Sequential rule mining |
| Zdroj: | International Journal of Software Innovation. 8:78-94 |
| ISSN: | 2166-7179 2166-7160 |
| DOI: | 10.4018/ijsi.2020100107 |
| Popis: | In the current scenario of the business world, the importance of data analytics is quite large. It certainly benefits the businesses in the decision-making process. Sequential rule mining can be widely utilized to extract important data having variety of applications like e-commerce, stock market analysis, etc. Predictive data analytics using the sequential rule mining consists of analyzing input sequences and finding sequential rules that can help businesses in decision making. This article presents an approach called M_TRuleGrowth that generates partially-ordered sequential rules efficiently. The authors conducted an experimental evaluation on real world dataset that provides strong evidence that M_TRuleGrowth performs better in terms of execution time. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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