NDLI: A Knowledge Mining Method for Continuous Data Based on Fuzzy C-Means Clustering and Rough Sets

Content Provider	IEEE Xplore Digital Library
Author	Xi Xu Qionghui Yao Min Shi
Copyright Year	2006
Description	Author affiliation: Dept. of Electr. Eng., Naval Univ. of Eng., Wuhan (Xi Xu; Qionghui Yao; Min Shi)
Abstract	For continuous data knowledge mining, fuzzy c-means clustering is used for dispersing continuous data to discrete variables and then the discretized data can be reduced by using rough set theory. In order to determine corresponding classes, one usually utilizes the maximum membership degree method. However when membership values are close together, above method can't reflect the essential characteristics of continuous data. In this paper, a new method, which is based on so-called membership superposition degree instead of maximum membership degree, is proposed. The method is applied to the knowledge mining of steamer axes vibration data. Result shows that the method can reflect the characteristics of continuous data
Starting Page	5846
Ending Page	5849
File Size	114414
Page Count	4
File Format	PDF
ISBN	1424403324
DOI	10.1109/WCICA.2006.1714198
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-06-21
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuzzy sets Rough sets Set theory Fuzzy set theory Data mining Medical diagnosis Data engineering Knowledge engineering Data processing Fault diagnosis Knowledge mining Fuzzy C-Means Rough set Membership superposition degree
Content Type	Text
Resource Type	Article

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