A Study on Adjustable Dissimilarity Measure for Efficient Piano Learning

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.

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A Novel Random Forest Dissimilarity Measure for Multi-View Learning

2020 25th International Conference on Pattern Recognition (ICPR) ◽

10.1109/icpr48806.2021.9412961 ◽

2021 ◽

Author(s):

Hongliu Cao ◽

Simon Bernard ◽

Robert Sabourin ◽

Laurent Heutte

Keyword(s):

Random Forest ◽

Dissimilarity Measure

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An affine invariant tensor dissimilarity measure and its applications to tensor-valued image segmentation

Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004. ◽

10.1109/cvpr.2004.1315036 ◽

2004 ◽

Cited By ~ 2

Author(s):

Zhizhou Wang ◽

B.C. Vemuri

Keyword(s):

Image Segmentation ◽

Dissimilarity Measure ◽

Affine Invariant ◽

Invariant Tensor

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

Decision Support Systems ◽

10.1016/j.dss.2011.06.002 ◽

2011 ◽

Vol 52 (1) ◽

pp. 133-141 ◽

Cited By ~ 81

Author(s):

Zhun-ga Liu ◽

Jean Dezert ◽

Quan Pan ◽

Grégoire Mercier

Keyword(s):

Dissimilarity Measure

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Efficient Time Series Clustering and Its Application to Social Network Mining

Journal of Intelligent Systems ◽

10.1515/jisys-2014-0005 ◽

2014 ◽

Vol 23 (2) ◽

pp. 213-229 ◽

Cited By ~ 2

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.

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