NDLI: Knowledge acquisition based on rough set and data mining

Content Provider	IEEE Xplore Digital Library
Author	Haifeng Guo Xiaoming Zhou Yulong Zhu
Copyright Year	2009
Description	Author affiliation: Shenyang Institute of Automation, Chinese Academy of Science, China (Xiaoming Zhou; Yulong Zhu) \|\| College of Information Science and Engineering, Shenyang Ligong University, China (Haifeng Guo)
Abstract	The case-based reasoning (CBR) becomes a novel paradigm that solves a new problem by remembering a previous similar situation and by reusing information and knowledge of that situation. However, the acquisition of case knowledge is a bottleneck within case-based reasoning. The use of rough set and data mining to discover knowledge from traditional database and to construct case base is desired. In this paper we discuss, in detail, the approach taken to acquire case knowledge. Rough set is used to preprocess the raw data that is noisy and redundant on the attribute. A Kohonen network is proposed to identify initial clusters within the data having been preprocessed. These clusters are then analyzed using C.45 and non-unique clusters are grouped to form concepts. Cases are then chosen from each of the identified concepts as well as outliers in the database. The results indicate that the proposed approach achieves a high reduction in the size of the case base.
Starting Page	126
Ending Page	128
File Size	1402205
Page Count	3
File Format	PDF
ISBN	9781424446902
DOI	10.1109/FBIE.2009.5405857
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-12-13
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Economic indicators Statistical analysis Knowledge acquisition Rough Set Data engineering Educational institutions Data Mining Data mining Information analysis CBR Production Case Knowledge Acquisition Principal component analysis Biomedical engineering
Content Type	Text
Resource Type	Article

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