Improved rough k-means clustering algorithm based on weighted distance measure with Gaussian function

Rough k-means clustering algorithm and its extensions are introduced and successfully applied to real-life data where clusters do not necessarily have crisp boundaries. Experiments with the rough k-means clustering algorithm have shown that it provides a reasonable set of lower and upper bounds for...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:International journal of computer mathematics Ročník 94; číslo 4; s. 663 - 675
Hlavní autoři: Zhang, Tengfei, Ma, Fumin
Médium: Journal Article
Jazyk:angličtina
Vydáno: Abingdon Taylor & Francis 03.04.2017
Taylor & Francis Ltd
Témata:
Algorithms
Approximation
Cluster analysis
Clustering
Clustering algorithm
Clusters
Computer simulation
Gaussian function
Mathematical analysis
Mathematical models
rough k-means
rough set theory
Routing
Vector quantization
weighted distance measure
ISSN:0020-7160, 1029-0265
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Rough k-means clustering algorithm and its extensions are introduced and successfully applied to real-life data where clusters do not necessarily have crisp boundaries. Experiments with the rough k-means clustering algorithm have shown that it provides a reasonable set of lower and upper bounds for a given dataset. However, the same weight was used for all the data objects in a lower or upper approximate set when computing the new centre for each cluster while the different impacts of the objects in a same approximation were ignored. An improved rough k-means clustering based on weighted distance measure with Gaussian function is proposed in this paper. The validity of this algorithm is demonstrated by simulation and experimental analysis.
Bibliografie:SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ObjectType-Article-1
ObjectType-Feature-2
content type line 23
ISSN:0020-7160
1029-0265
DOI:10.1080/00207160.2015.1124099