scholarly journals Coarse-refinement dilemma: on generalization bounds for data clustering

2021 ◽  
pp. 115399
Author(s):  
Yule Vaz ◽  
Rodrigo Fernandes de Mello ◽  
Carlos Henrique Grossi Ferreira
Keyword(s):  
Data Clustering ◽  
Generalization Bounds

Balanced Data Clustering Algorithm for Both Hard and Soft Clustering

2018 ◽  
Vol 6 (2) ◽  
pp. 176-183
Author(s):  
Purnendu Das ◽  
◽  
Bishwa Ranjan Roy ◽  
Saptarshi Paul ◽  
◽  
...  
Keyword(s):  
Data Clustering ◽  
Clustering Algorithm ◽  
Soft Clustering

DATA CLUSTERING DURING A GEOLOGICAL-FIELD ANALYSIS

2020 ◽  
pp. 49-52
Author(s):  
M.R. Dulkarnaev ◽  
◽  
R.R. Yunusov ◽  
I.V. Ryabov ◽  
P.Yu. Lobanov ◽  
...  
Keyword(s):  
Data Clustering ◽  
Field Analysis

WSN Localization Method Using Interval Data Clustering

2012 ◽  
Vol 38 (7) ◽  
pp. 1190 ◽  
Author(s):  
Yu PENG ◽  
Qing-Hua LUO ◽  
Dan WANG ◽  
Xi-Yuan PENG
Keyword(s):  
Data Clustering ◽  
Interval Data ◽  
Localization Method

scholarly journals A General Framework for Mixed and Incomplete Data Clustering Based on Swarm Intelligence Algorithms

Mathematics ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 786
Author(s):  
Yenny Villuendas-Rey ◽  
Eley Barroso-Cubas ◽  
Oscar Camacho-Nieto ◽  
Cornelio Yáñez-Márquez
Keyword(s):  
Swarm Intelligence ◽  
Data Clustering ◽  
Incomplete Data ◽  
Missing Values ◽  
Clustering Algorithms ◽  
Bat Algorithm ◽  
Hybrid Features ◽  
Bee Colony ◽  
Learning Tasks ◽  
Clustering Data

Swarm intelligence has appeared as an active field for solving numerous machine-learning tasks. In this paper, we address the problem of clustering data with missing values, where the patterns are described by mixed (or hybrid) features. We introduce a generic modification to three swarm intelligence algorithms (Artificial Bee Colony, Firefly Algorithm, and Novel Bat Algorithm). We experimentally obtain the adequate values of the parameters for these three modified algorithms, with the purpose of applying them in the clustering task. We also provide an unbiased comparison among several metaheuristics based clustering algorithms, concluding that the clusters obtained by our proposals are highly representative of the “natural structure” of data.

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