A dynamic cluster maintenance system for information retrieval
SIGIR '87 Proceedings of the 10th annual international ACM SIGIR conference on Research and development in information retrieval
Algorithms for clustering data
Algorithms for clustering data
The formation and use of abstract concepts in design
Concept formation knowledge and experience in unsupervised learning
Vector quantization and signal compression
Vector quantization and signal compression
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Bayesian classification (AutoClass): theory and results
Advances in knowledge discovery and data mining
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
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
Experiments with Incremental Concept Formation: UNIMEM
Machine Learning
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Improving fuzzy c-means clustering based on feature-weight learning
Pattern Recognition Letters
Fuzzy clustering of categorical data using fuzzy centroids
Pattern Recognition Letters
ITERATE: a conceptual clustering algorithm for data mining
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Fuzzy clustering for symbolic data
IEEE Transactions on Fuzzy Systems
A fuzzy k-modes algorithm for clustering categorical data
IEEE Transactions on Fuzzy Systems
A note on the Gustafson-Kessel and adaptive fuzzy clustering algorithms
IEEE Transactions on Fuzzy Systems
A fuzzy k-prototype clustering algorithm for mixed numeric and categorical data
Knowledge-Based Systems
| Hi-index | 0.00 |
In many applications numeric as well as categorical features describe the data objects. A variety of algorithms have been proposed for clustering if fuzzy partitions and descriptive cluster prototypes are desired. However, most of these methods are designed for data sets with variables measured in the same scale type (only categorical, or only numeric). We have developed probabilistic distance measure to compute significance of attributes for numeric data, and distance between two categorical values. We used this distance measure with the cluster center definition proposed by Yasser El-Sonbaty and M. A. Ismail [26] to propose Fuzzy-c mean type clustering algorithm for mixed attributes data. The results of the application of the new algorithm show that new technique is quite encouraging.