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Entropy and Similarity Measure Design for High Dimensional Data with Spatial Information
- 1. Abstract of Applied Sciences and Engineering, 2015, Vol.3 DOI: 10.18488/journal.1001/2015.3/1001.3 3rd International Scientific Conference on Applied Sciences and Engineering 27-28 July, 2015 Lebua Hotels and Resorts, 1055 Silom Road, Bangrak, Bangkok 10500 Thailand Conference Website: www.scihost.org 4 Paper ID: 23/15/3 rd ISCASE Entropy and Similarity Measure Design for High Dimensional Data with Spatial Information Sanghyuk Lee1 --- Kyeongsoo Kim2 --- Eng Gee Lim3 1,2,3 Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, Jiangsu, China Abstract Research on entropy and similarity measures for high dimensional data was carried out with spatial information. General definition such as distance measure was also applied to high dimensional data. Designed entropy and similarity measure are applied to fuzzy data that has uncertainty. We also derived the summation of similarity measure and entropy between fuzzy set and the corresponding ordinary set, and the summation constitutes degree of uncertainty and certainty of data. As a result, we derived a similarity measure from entropy realization, and showed the maximum similar value can be obtained using minimum entropy by simple example. Keywords: Similarity measure, Entropy, Spatial information, High dimension data