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Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

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Combined mining: discovering informative knowledge in complex data.

Longbing Cao¹, Huaifeng Zhang, Yanchang Zhao

¹University of Technology, Sydney (UTS), Sydney, NSW, Australia.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|May 20, 2011

Summary

This summary is machine-generated.

Combined mining offers a flexible approach to extract valuable insights from complex enterprise data by integrating multiple data sources, features, or methods. This method uncovers novel patterns for improved decision-making in real-world applications.

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Area of Science:

Data Mining and Knowledge Discovery
Business Intelligence
Computational Science

Background:

Enterprise data mining often involves complex, heterogeneous data sources, limiting single-method approaches.
Extracting comprehensive knowledge requires integrating information from multiple business lines for practical decision-making.
Existing methods struggle to mine patterns that combine multiple data aspects efficiently.

Purpose of the Study:

To propose combined mining as a general framework for discovering informative patterns from complex enterprise data.
To develop generalizable frameworks for multifeature, multisource, and multimethod combined mining.
To demonstrate the application and flexibility of combined mining in real-world scenarios.

Main Methods:

Summarizing general frameworks, paradigms, and processes for combined mining.
Developing approaches for multifeature combined mining, multisource combined mining, and multimethod combined mining.
Utilizing case studies to test and validate the proposed combined mining frameworks.

Main Results:

Introduction of novel combined mining frameworks adaptable to various data mining challenges.
Generation of new pattern types, such as incremental cluster patterns, not achievable with existing methods.
Successful identification of combined patterns for government debt prevention and service improvement.

Conclusions:

Combined mining provides a flexible and powerful approach for extracting deeper insights from complex enterprise data.
The proposed frameworks demonstrate broad applicability and instantiation capability across diverse business settings.
Combined mining enhances decision-making by uncovering informative patterns that integrate multiple data dimensions.