Algorithms for clustering data
Algorithms for clustering data
Unsupervised Optimal Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
The formation and use of abstract concepts in design
Concept formation knowledge and experience in unsupervised learning
An Evaluation of Statistical Approaches to Text Categorization
Information Retrieval
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values
Data Mining and Knowledge Discovery
Unsupervised Learning with Mixed Numeric and Nominal Data
IEEE Transactions on Knowledge and Data Engineering
Mixture model clustering for mixed data with missing information
Computational Statistics & Data Analysis
Feature Weighting in k-Means Clustering
Machine Learning
ROCK: A Robust Clustering Algorithm for Categorical Attributes
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Robust fuzzy clustering using mixtures of Student's-t distributions
Pattern Recognition Letters
A genetic fuzzy k-Modes algorithm for clustering categorical data
Expert Systems with Applications: An International Journal
Factor analysis latent subspace modeling and robust fuzzy clustering using t-distributions
IEEE Transactions on Fuzzy Systems
A Clustering Performance Measure Based on Fuzzy Set Decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Regularized Linear Fuzzy Clustering and Probabilistic PCA Mixture Models
IEEE Transactions on Fuzzy Systems
A fuzzy k-prototype clustering algorithm for mixed numeric and categorical data
Knowledge-Based Systems
A data mining approach to knowledge discovery from multidimensional cube structures
Knowledge-Based Systems
CRUDAW: a novel fuzzy technique for clustering records following user defined attribute weights
AusDM '12 Proceedings of the Tenth Australasian Data Mining Conference - Volume 134
| Hi-index | 12.05 |
Gath-Geva (GG) algorithm is one of the most popular methodologies for fuzzy c-means (FCM)-type clustering of data comprising numeric attributes; it is based on the assumption of data deriving from clusters of Gaussian form, a much more flexible construction compared to the spherical clusters assumption of the original FCM. In this paper, we introduce an extension of the GG algorithm to allow for the effective handling of data with mixed numeric and categorical attributes. Traditionally, fuzzy clustering of such data is conducted by means of the fuzzy k-prototypes algorithm, which merely consists in the execution of the original FCM algorithm using a different dissimilarity functional, suitable for attributes with mixed numeric and categorical attributes. On the contrary, in this work we provide a novel FCM-type algorithm employing a fully probabilistic dissimilarity functional for handling data with mixed-type attributes. Our approach utilizes a fuzzy objective function regularized by Kullback-Leibler (KL) divergence information, and is formulated on the basis of a set of probabilistic assumptions regarding the form of the derived clusters. We evaluate the efficacy of the proposed approach using benchmark data, and we compare it with competing fuzzy and non-fuzzy clustering algorithms.