A New Fuzzy Clustering Validity Index With a Median Factor for Centroid-Based Clustering

Determining the number of clusters, which is usually approved by domain experts or evaluated by clustering validity indexes, is an important issue in clustering analysis. This study discusses the effectiveness of clustering validity indexes for centroid-based partitional clustering algorithms. Most...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:IEEE transactions on fuzzy systems Ročník 23; číslo 3; s. 701 - 718
Hlavní autori: Wu, Chih-Hung, Ouyang, Chen-Sen, Chen, Li-Wen, Lu, Li-Wei
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: IEEE 01.06.2015
Predmet:
Algorithm design and analysis
Clustering algorithms
Indexes
Open systems
Partitioning algorithms
Principal component analysis
Signal processing algorithms
clustering validity index (CVI)
Clustering analysis
partitional clustering algorithm
fuzzy c-means (FCM) clustering algorithm
ISSN:1063-6706, 1941-0034
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:Determining the number of clusters, which is usually approved by domain experts or evaluated by clustering validity indexes, is an important issue in clustering analysis. This study discusses the effectiveness of clustering validity indexes for centroid-based partitional clustering algorithms. Most general-purpose clustering validity indexes take the minimum/maximum distance between a pair of data objects, a pair of cluster centroids, or an object and a centroid as an important evaluation factor; however, they may present unstable results, especially when two centroids are allocated closely. To alleviate this problem, a new clustering validity index, which is termed the Wu-and-Li index (WLI), is proposed in this paper. Our proposed WLI partially allows, to some extent, the existence of closely allocated centroids in the clustering results by considering not only the minimum but the median distances between a pair of centroids as well; therefore possessing better stability. The performances of WLI and some existing clustering validity indexes are evaluated and compared by running the fuzzy c-means algorithm for clustering various types of datasets, including artificial datasets, UCI datasets, and images. Experimental results have shown that WLI has the more accurate and satisfactory performance than other indexes.
ISSN:1063-6706
1941-0034
DOI:10.1109/TFUZZ.2014.2322495