Domain-Driven Data Mining: Challenges and Prospects

@article{Cao2010DomainDrivenDM,
  title={Domain-Driven Data Mining: Challenges and Prospects},
  author={Longbing Cao},
  journal={IEEE Transactions on Knowledge and Data Engineering},
  year={2010},
  volume={22},
  pages={755-769}
}
  • Longbing Cao
  • Published 1 June 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… 

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References

SHOWING 1-10 OF 40 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.

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TLDR
Domain Driven Data Mining enhances the actionability and wider deployment of existing data-centered data mining through a combination of domain and business oriented factors, constraints and intelligence.

The Evolution of KDD: towards Domain-Driven Data Mining

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 be

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.

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.

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.

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.

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.

Agent Mining: The Synergy of Agents and Data Mining

TLDR
An overall perspective of the driving forces, theoretical underpinnings, main research issues, and application domains of this field, while addressing the state-of-the-art of agent mining research and development is given.

Metalearning - Applications to Data Mining

TLDR
This book discusses several approaches to obtaining knowledge concerning the performance of machine learning and data mining algorithms and shows how this knowledge can be reused to select, combine, compose and adapt both algorithms and models to yield faster, more effective solutions to data mining problems.