DMCA
Domain-Driven Data Mining: Challenges and Prospects
| Citations: | 13 - 3 self |
BibTeX
@MISC{Cao_domain-drivendata,
author = {Longbing Cao and Senior Member},
title = {Domain-Driven Data Mining: Challenges and Prospects},
year = {}
}
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Abstract
Abstract—Traditional data mining research mainly focus]es on developing, demonstrating, and pushing the use of specific algorithms and models. The process of data mining stops at pattern identification. Consequently, a widely seen fact is that 1) many algorithms have been designed of which very few are repeatable and executable in the real world, 2) often many patterns are mined but a major proportion of them are either commonsense or of no particular interest to business, and 3) end users generally cannot easily understand and take them over for business use. In summary, we see that the findings are not actionable, and lack soft power in solving real-world complex problems. Thorough efforts are essential for promoting the actionability of knowledge discovery in realworld smart decision making. To this end, domain-driven data mining (D 3 M) has been proposed to tackle the above issues, and promote the paradigm shift from “data-centered knowledge discovery ” to “domain-driven, actionable knowledge delivery. ” In D 3 M, ubiquitous intelligence is incorporated into the mining process and models, and a corresponding problem-solving system is formed as the space for knowledge discovery and delivery. Based on our related work, this paper presents an overview of driving forces, theoretical frameworks, architectures, techniques, case studies, and open issues of D 3 M. We understand D 3 M discloses many critical issues with no thorough and mature solutions available for now, which indicates the challenges and prospects for this new topic. Index Terms—Data mining, domain-driven data mining (D 3 M), actionable knowledge discovery and delivery. Ç 1
Keyphrases
domain-driven data mining knowledge discovery actionable knowledge discovery major proportion paradigm shift ubiquitous intelligence data-centered knowledge discovery soft power discloses many critical issue related work case study real-world complex problem abstract traditional data mining research mature solution new topic many algorithm index term data mining realworld smart decision making actionable knowledge delivery end user real world specific algorithm particular interest theoretical framework corresponding problem-solving system pattern identification open issue mining process business use many pattern
