NDLI: Knowledge discovery based on fuzzy rough set theory by inclusion degrees for space load forecasting

Content Provider	IEEE Xplore Digital Library
Author	Weiguo Li Hong Li Haoqing Xiong Guoyi Xu Jiangfeng Zou Weicheng Yu
Copyright Year	2009
Abstract	A spatial load forecasting (SLF) model realized by decision table is proposed, in which the setting of condition and decision attributes is based on fuzzy rough set (FRS) theory. The proposed SLF model has the capability of coping with not only natural geographic factors but also abrupt issues. Using data mining (DM) technology, a large amount of data related to land-use-change can be analyzed and managed in order to avoid the turbulence occurring in data management. In the model proposed, the problems investigated in this paper are as follows: 1. An iterated algorithm for progressive sampling based on “boundary region” of rough-set (RS) theory is developed; 2. Fuzzy division for problem region, by means of knowledge data in database (KDD) is proposed; 3. Attributes with continuous value is coped with to be convenient with information table based on FRS so that the output of SLF is better than that based on RS theory. The test results show that the proposed method is more feasible for SLF in complex situations than traditional methods.
Starting Page	1
Ending Page	7
File Size	656253
Page Count	7
File Format	PDF
ISBN	9781424449347
DOI	10.1109/SUPERGEN.2009.5347968
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-04-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Predictive models Data mining Distribution planning Technology management Knowledge data in database (KDD) Load forecasting Databases Fuzzy clustering Inclusion degrees Set theory Sampling methods Fuzzy set theory Delta modulation Fuzzy rough set Spatial load forecasting (SLF) Load modeling
Content Type	Text
Resource Type	Article

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