scholarly journals The Weakly-Labeled Rand Index

2021 ◽  
Author(s):  
Dylan Stewart ◽  
Anna Hampton ◽  
Alina Zare ◽  
Jeff Dale ◽  
James Keller
Keyword(s):  
Rand Index

Fuzzy Generalization and Comparisons for the Rand Index

2017 ◽  
Vol 33 (5) ◽  
pp. 901-927 ◽  
Author(s):  
Miin-Shen Yang ◽  
Chiou-Cherng Yeh
Keyword(s):  
Rand Index

A computer program to calculate Hubert and Arabie's adjusted rand index

1996 ◽  
Vol 13 (1) ◽  
pp. 169-172 ◽  
Author(s):  
Robert Saltstone ◽  
Ken Stange
Keyword(s):  
Computer Program ◽  
Rand Index ◽  
Adjusted Rand Index

Families of Triangular Norm-Based Kernel Functions and Their Application to Kernel k-Means

2017 ◽  
Vol 21 (3) ◽  
pp. 534-542
Author(s):  
Kazushi Okamoto ◽  
Keyword(s):  
Feature Space ◽  
Kernel Functions ◽  
Rand Index ◽  
Adjusted Rand Index ◽  
Data Sets ◽  
Linear Kernel ◽  
Triangular Norm ◽  
Higher Dimensional ◽  
Rbf Kernel ◽  
Map Data

This study proposes the concept of families of triangular norm (t-norm)-based kernel functions, and discusses their positive-definite property and the conditions for applicable t-norms. A clustering experiment with kernel k-means is performed in order to analyze the characteristics of the proposed concept, as well as the effects of the t-norm and parameter selections. It is evaluated that the clusters obtained in terms of the adjusted rand index and the experimental results suggested the following : (1) the adjusted rand index values obtained by the proposed method were almost the same or higher than those produced using the linear kernel for all of the data sets; (2) the proposed method slightly improved the adjusted rand index values for some data sets compared with the radial basis function (RBF) kernel; (3) the proposed method tended to map data to a higher dimensional feature space than the linear kernel but the dimension was lower than that using the RBF kernel.

scholarly journals Detecting genomic regions associated with a disease using variability functions and Adjusted Rand Index

2011 ◽  
Vol 12 (Suppl 9) ◽  
pp. S9 ◽  
Author(s):  
Dunarel Badescu ◽  
Alix Boc ◽  
Abdoulaye Diallo ◽  
Vladimir Makarenkov
Keyword(s):  
Rand Index ◽  
Adjusted Rand Index ◽  
Genomic Regions

scholarly journals Clustering for Probability Density Functions by New k-Medoids Method

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
D. Ho-Kieu ◽  
T. Vo-Van ◽  
T. Nguyen-Trang
Keyword(s):  
Probability Density ◽  
Clustering Algorithm ◽  
Real Life ◽  
Probability Density Functions ◽  
Rand Index ◽  
Computational Time ◽  
Adjusted Rand Index ◽  
Density Functions ◽  
Potential Applications ◽  
Iteration Number

This paper proposes a novel and efficient clustering algorithm for probability density functions based on k-medoids. Further, a scheme used for selecting the powerful initial medoids is suggested, which speeds up the computational time significantly. Also, a general proof for convergence of the proposed algorithm is presented. The effectiveness and feasibility of the proposed algorithm are verified and compared with various existing algorithms through both artificial and real datasets in terms of adjusted Rand index, computational time, and iteration number. The numerical results reveal an outstanding performance of the proposed algorithm as well as its potential applications in real life.

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