Research on the Extension Mining Model of Implication Type Data with Multi Factors Based on Extension Theory

다국어 초록 (Multilingual Abstract)

The author conducts research on the mining problem of implication type data aiming at the characteristics of implication, uncertainty, nonlinearity, dynamic, complexity existing in the process of data mining and establishes an extension mining model of implication type data with multi factors based on extension theory. This model, first of all, carries out implication analysis on the implication type data and builds corresponding implication set; then, the author conducts extension classification on the hypogynous factor in the implication set and builds classical field and segment field of the epigynous factorin the implication set based on extension type divided; the author also respectively builds the correlation function and extension goodness-of-fit model between targeted mining object and classical field of epigynous factorin the implication set, acquires comprehensive extension goodness-of-fit considering the weight of epigynous factors, which, in other words, determines the degree of closeness between targeted mining object and extension type and thus achieves the mining of implication type data. Finally, the author demonstrates the feasibility of this model by explaining and verifying the venture capital case of an enterprise.

목차 (Table of Contents)

• Abstract
• 1. Introduction
• 2. Extension Data Mining Process Model in Complex System
• 2.1. The Generation of Implication Set
• 2.2. Matter Element Modeling
• 2.3. Classical Field and Segment Field
• 2.4. Implication Index Weight
• 2.5. Extension Goodness-of-Fit
• 2.6. The Realization of Data Mining Algorithm
• 3. Application Analysis
• 4. Conclusions
• Acknowledgements
• References