Journal Article10.1109/34.85677
A validity measure for fuzzy clustering
X.L. Xie,Gerardo Beni +1 more
3.4K
TL;DR: The authors present a fuzzy validity criterion based on a validity function which identifies compact and separate fuzzy c-partitions without assumptions as to the number of substructures inherent in the data.
read more
Abstract: The authors present a fuzzy validity criterion based on a validity function which identifies compact and separate fuzzy c-partitions without assumptions as to the number of substructures inherent in the data. This function depends on the data set, geometric distance measure, distance between cluster centroids and more importantly on the fuzzy partition generated by any fuzzy algorithm used. The function is mathematically justified via its relationship to a well-defined hard clustering validity function, the separation index for which the condition of uniqueness has already been established. The performance of this validity function compares favorably to that of several others. The application of this validity function to color image segmentation in a computer color vision system for recognition of IC wafer defects which are otherwise impossible to detect using gray-scale image processing is discussed. >
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Rough-fuzzy clustering: an application to medical imagery
Sushmita Mitra,Bishal Barman +1 more
- 17 May 2008
TL;DR: A novel application of rough-fuzzy clustering is presented for synthetic as well as CT scan images of the brain, and it is observed that the algorithm generates good prototypes even in the presence of outliers.
25
A new validity measure for a correlation-based fuzzy c-means clustering algorithm
Mingrui Zhang,Wei Zhang,Hugues Sicotte,Ping Yang +3 more
- 13 Nov 2009
TL;DR: A new measure for a fuzzy c-means algorithm which uses a Pearson correlation in its distance metrics is developed and designed with within-cluster sum of square, and makes use of fuzzy memberships.
•Book
SmartParticipation: A Fuzzy-Based Recommender System for Political Community-Building
Luis Terán
- 18 Jun 2014
TL;DR: A fuzzy-based recommender system architecture for stimulating political participation and collaboration is proposed and an evaluation framework for e Participation is presented, which allows to analyze different projects and their development towards the enhancement of citizen's participation and empowerment.
25
On the reliability of multipath cluster estimation in realistic channel data sets
Christian Schneider,Maysam Ibraheam,Stephan Hafner,Martin Kaske,Matthias Hein,Reiner S. Thoma +5 more
- 06 Apr 2014
TL;DR: In this contribution a framework for evaluation and development of different cluster algorithms is discussed, and a novel hierarchical algorithm is introduced and compared to standard K-mean and Fuzzy-C-means algorithms.
24
Patent
Convolution filtering of similarity data for visual display of enhanced image
Christopher L. Stork,Bradley T. Wyman +1 more
- 24 Nov 2000
TL;DR: In this article, a similarity value of data elements is modified to provide an improved image of an object using an appropriate sensor, data is collected from an object and stored as a plurality of discrete data points, the data points are compared to each other to determine how similar the data properties of one data element are to another data element.
24
References
•Book
Pattern Recognition with Fuzzy Objective Function Algorithms
James C. Bezdek
- 31 Jul 1981
TL;DR: Books, as a source that may involve the facts, opinion, literature, religion, and many others are the great friends to join with, becomes what you need to get.
17.9K
Well-Separated Clusters and Optimal Fuzzy Partitions
J. C. Dunn
- 01 Jan 1974
TL;DR: In this article, two separation indices for partitions P = {X1, …, Xk} of a finite data set X in a general inner product space are considered, and it is shown that as the indices of p' increase without bound, the characteristic functions of Xi' in P' are approximated more and more closely by the membership functions in fuzzy partitions which minimize certain fuzzy extensions of the k-means squared error criterion function.
2.4K