A COMPARISON OF TWO FUZZY CLUSTERING TECHNIQUES
- In fuzzy clustering, unlike hard clustering, depending on the membership value, a single object may belong exactly to one cluster or partially to more than one cluster. Out of a number of fuzzy clustering techniques Bezdek’s Fuzzy C-Means and GustafsonKessel clustering techniques are well kno...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Faculty of Applied Management, Economics and Finance – MEF, Belgrade, University Business Academy in Novi Sad
2013-10-01
|
| Series: | Journal of Process Management and New Technologies |
| Subjects: | |
| Online Access: | http://www.japmnt.com/images/Volume%201/Issue%204/A%20COMPARISON%20OF%20TWO%20FUZZY%20CLUSTERING%20TECHNIQUES.pdf |
| Summary: | - In fuzzy clustering, unlike hard clustering,
depending on the membership value, a single object
may belong exactly to one cluster or partially to more
than one cluster. Out of a number of fuzzy clustering
techniques Bezdek’s Fuzzy C-Means and GustafsonKessel
clustering techniques are well known where
Euclidian distance and Mahalanobis distance are used
respectively as a measure of similarity. We have
applied these two fuzzy clustering techniques on a
dataset of individual differences consisting of fifty
feature vectors of dimension (feature) three. Based on
some validity measures we have tried to see the
performances of these two clustering techniques from
three different aspects- first, by initializing the
membership values of the feature vectors considering
the values of the three features separately one at a
time, secondly, by changing the number of the
predefined clusters and thirdly, by changing the size
of the dataset. |
|---|---|
| ISSN: | 2334-735X 2334-7449 |