Domain-Driven Data Mining: Challenges and Prospects

@article{Cao2010DomainDrivenDM,
  title={Domain-Driven Data Mining: Challenges and Prospects},
  author={L. Cao},
  journal={IEEE Transactions on Knowledge and Data Engineering},
  year={2010},
  volume={22},
  pages={755-769}
}
  • L. Cao
  • Published 2010
  • Computer Science
  • IEEE Transactions on Knowledge and Data Engineering
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… Expand
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References

SHOWING 1-10 OF 44 REFERENCES
Domain-Driven Data Mining: A Practical Methodology
TLDR
This article proposes a practical data mining methodology referred to as domain-driven data mining, which targets actionable knowledge discovery in a constrained environment for satisfying user preference and illustrates some examples in mining actionable correlations in Australian Stock Exchange. Expand
Domain Driven Data Mining
In the present thriving global economy a need has evolved for complex data analysis to enhance an organizations production systems, decision-making tactics, and performance. In turn, data mining hasExpand
The Evolution of KDD: towards Domain-Driven Data Mining
  • L. Cao, C. Zhang
  • Computer Science
  • Int. J. Pattern Recognit. Artif. Intell.
  • 2007
Traditionally, data mining is an autonomous data-driven trial-and-error process. Its typical task is to let data tell a story disclosing hidden information, in which domain intelligence may not beExpand
Flexible Frameworks for Actionable Knowledge Discovery
TLDR
Substantial experiments show that the proposed frameworks are sufficiently general, flexible, and practical to tackle many complex problems and applications by extracting actionable deliverables for instant decision making. Expand
Mining the Knowledge Mine: The Hot Spots Methodology for Mining Large Real World Databases
TLDR
The hot spots methodology is presented, adopting a multi-strategy and interactive approach to help focus on the important nuggets of knowledge in insurance and fraud applications. Expand
Extracting Actionable Knowledge from Decision Trees
TLDR
Novel algorithms that suggest actions to change customers from an undesired status to a desired one while maximizing an objective function: the expected net profit are presented. Expand
Domain-Driven, Actionable Knowledge Discovery
TLDR
Data mining increasingly faces complex challenges in the real-life world of business problems and needs and both researchers and practitioners are realizing the importance of domain knowledge to close this gap and develop actionable knowledge for real user needs. Expand
Report on the SIGKDD-2002 panel the perfect data mining tool: interactive or automated?
TLDR
The role of human involvement in the data mining process enables domain knowledge transfer and the use of the human’s perceptual capabiliti es and the vast amount of data to be mined today makes realtime interactivity hard to achieve and unnecessarily burdens the user to perform tasks that may be done automatically. Expand
Applying Objective Interestingness Measures in Data Mining Systems
TLDR
A two-step process for ranking the interestingness of discovered patterns that utilizes the chi-square test for independence in the first step and objective measures of interestingness in the second step is described that can be applied to ranking characterized/generalized association rules and data cubes. Expand
Efficient mining of emerging patterns: discovering trends and differences
  • Guozhu Dong, Jinyan Li
  • Computer Science
  • KDD '99
  • 1999
TLDR
It is believed that EPs with low to medium support, such as 1%-20%, can give useful new insights and guidance to experts, in even “well understood” applications. Expand
...
1
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3
4
5
...