scholarly journals Approximate Matching in ACSM Dissimilarity Measure

2016 ◽  
Vol 96 ◽  
pp. 1479-1488 ◽  
Author(s):  
Alessia Amelio
Keyword(s):  
Dissimilarity Measure ◽  
Approximate Matching

Renewable energy selection based on a new entropy and dissimilarity measure on an interval-valued neutrosophic set

2021 ◽  
pp. 1-18
Author(s):  
ShuoYan Chou ◽  
Truong ThiThuy Duong ◽  
Nguyen Xuan Thao
Keyword(s):  
Decision Making ◽  
Renewable Energy ◽  
Membership Function ◽  
Fossil Fuels ◽  
Multiple Criteria Decision Making ◽  
Clean Energy ◽  
Dissimilarity Measure ◽  
Neutrosophic Set ◽  
Trade Offs ◽  
And Performance

Energy plays a central part in economic development, yet alongside fossil fuels bring vast environmental impact. In recent years, renewable energy has gradually become a viable source for clean energy to alleviate and decouple with a negative connotation. Different types of renewable energy are not without trade-offs beyond costs and performance. Multiple-criteria decision-making (MCDM) has become one of the most prominent tools in making decisions with multiple conflicting criteria existing in many complex real-world problems. Information obtained for decision making may be ambiguous or uncertain. Neutrosophic is an extension of fuzzy set types with three membership functions: truth membership function, falsity membership function and indeterminacy membership function. It is a useful tool when dealing with uncertainty issues. Entropy measures the uncertainty of information under neutrosophic circumstances which can be used to identify the weights of criteria in MCDM model. Meanwhile, the dissimilarity measure is useful in dealing with the ranking of alternatives in term of distance. This article proposes to build a new entropy and dissimilarity measure as well as to construct a novel MCDM model based on them to improve the inclusiveness of the perspectives for decision making. In this paper, we also give out a case study of using this model through the process of a renewable energy selection scenario in Taiwan performed and assessed.

scholarly journals A Novel Random Forest Dissimilarity Measure for Multi-View Learning

2021 ◽  
Author(s):  
Hongliu Cao ◽  
Simon Bernard ◽  
Robert Sabourin ◽  
Laurent Heutte
Keyword(s):  
Random Forest ◽  
Dissimilarity Measure

scholarly journals Combination of sources of evidence with different discounting factors based on a new dissimilarity measure

2011 ◽  
Vol 52 (1) ◽  
pp. 133-141 ◽  
Author(s):  
Zhun-ga Liu ◽  
Jean Dezert ◽  
Quan Pan ◽  
Grégoire Mercier
Keyword(s):  
Dissimilarity Measure

Approximate Matching of Digital Point Sets Using a Novel Angular Tree

2009 ◽  
Vol 31 (5) ◽  
pp. 769-782 ◽  
Author(s):  
P. Bhowmick ◽  
R.K. Pradhan ◽  
B.B. Bhattacharya
Keyword(s):  
Point Sets ◽  
Approximate Matching

Efficient Time Series Clustering and Its Application to Social Network Mining

2014 ◽  
Vol 23 (2) ◽  
pp. 213-229 ◽  
Author(s):  
Cangqi Zhou ◽  
Qianchuan Zhao
Keyword(s):  
Time Series ◽  
Social Network ◽  
Information Diffusion ◽  
Time Series Data ◽  
Online Social Network ◽  
Dissimilarity Measure ◽  
Series Data ◽  
Formation Mechanisms ◽  
Network Analyses ◽  
Definition Of

AbstractMining time series data is of great significance in various areas. To efficiently find representative patterns in these data, this article focuses on the definition of a valid dissimilarity measure and the acceleration of partitioning clustering, a common group of techniques used to discover typical shapes of time series. Dissimilarity measure is a crucial component in clustering. It is required, by some particular applications, to be invariant to specific transformations. The rationale for using the angle between two time series to define a dissimilarity is analyzed. Moreover, our proposed measure satisfies the triangle inequality with specific restrictions. This property can be employed to accelerate clustering. An integrated algorithm is proposed. The experiments show that angle-based dissimilarity captures the essence of time series patterns that are invariant to amplitude scaling. In addition, the accelerated algorithm outperforms the standard one as redundancies are pruned. Our approach has been applied to discover typical patterns of information diffusion in an online social network. Analyses revealed the formation mechanisms of different patterns.

A dissimilarity measure for the k-Modes clustering algorithm

2012 ◽  
Vol 26 ◽  
pp. 120-127 ◽  
Author(s):  
Fuyuan Cao ◽  
Jiye Liang ◽  
Deyu Li ◽  
Liang Bai ◽  
Chuangyin Dang
Keyword(s):  
Clustering Algorithm ◽  
Dissimilarity Measure
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