Integrating fuzzy data mining and fuzzy artificial neural networks for discovering implicit knowledge

Mu-Jung Huang, Yee Lin Tsou, Show Chin Lee

Research output: Contribution to journalArticle

48 Citations (Scopus)

Abstract

This study proposes a knowledge discovery model that integrates the modification of the fuzzy transaction data-mining algorithm (MFTDA) and the Adaptive-Network-Based Fuzzy Inference Systems (ANFIS) for discovering implicit knowledge in the fuzzy database more efficiently and presenting it more concisely. A prototype was built for testing the feasibility of the model. The testing data are from a company's human resource management department. The results indicated that the generated rules (knowledge) are useful in supporting the company to predict its employees' future performance and then assign proper persons for appropriate positions and projects. Furthermore, the convergence of ANFIS in the model was proven to be more efficient than a generic fuzzy artificial neural network.

Original languageEnglish
Pages (from-to)396-403
Number of pages8
JournalKnowledge-Based Systems
Volume19
Issue number6
DOIs
Publication statusPublished - 2006 Oct 1

All Science Journal Classification (ASJC) codes

  • Management Information Systems
  • Software
  • Information Systems and Management
  • Artificial Intelligence

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