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American Journal of Computer Science and Information Engineering

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An Approach to Interval-Valued Complex Information Mining

American Journal of Computer Science and Information Engineering
Vol.5 , No. 1, Publication Date: Feb. 12, 2018, Page: 1-8

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Authors

[1]	Yunfei Yin, Key Lab. of Dependable Service Computing in Cyber Physical Society of Ministry of Education, Chongqing, P. R. China; College of Computer Science, Chongqing University, Chongqing, P. R. China.
[2]	Huan Liu, College of Computer Science, Chongqing University, Chongqing, P. R. China.
[3]	Xuesong Feng, College of Computer Science, Chongqing University, Chongqing, P. R. China.
[4]	Chunmei Ning, College of Computer Science, Chongqing University, Chongqing, P. R. China.

Abstract

It is a difficult issue for complex information mining, especially for interval-valued information mining. In this paper, an iterative interval-valued mining model is proposed that classifies the interval-valued information into three types, viz., "Interval-Value", "Interval-Interval" and "Interval-Matrix". As to the "Interval-Value" information, the "Netting"→"Type-I clustering"→"Type-II clustering" method is adopted to handle; as to the "Interval-Interval" information, the interval medium clustering method is adopted to handle; as to the "Interval-Matrix", the matrix threshold clustering method is adopted to handle. Finally, based on these three interval-valued clustering methods, the iterative data mining model has been designed. The motivation is to mine the interval-valued association rules from the interval-valued complex information. By the experimental study in the typical interval-valued information fields, the experimental results show the effectiveness and efficiency of the models and algorithms.

Keywords

Data Mining, Interval-Valued, Clustering, Model of Design

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