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
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Unsupervised feature selection using a neuro-fuzzy approach
Pattern Recognition Letters
ACM Computing Surveys (CSUR)
Data mining: concepts and techniques
Data mining: concepts and techniques
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Data Mining: Introductory and Advanced Topics
Data Mining: Introductory and Advanced Topics
COOLCAT: an entropy-based algorithm for categorical clustering
Proceedings of the eleventh international conference on Information and knowledge management
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
Improving Performance of Similarity-Based Clustering by Feature Weight Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fuzzy clustering of categorical data using fuzzy centroids
Pattern Recognition Letters
A k-mean clustering algorithm for mixed numeric and categorical data
Data & Knowledge Engineering
Incremental clustering of mixed data based on distance hierarchy
Expert Systems with Applications: An International Journal
G-ANMI: A mutual information based genetic clustering algorithm for categorical data
Knowledge-Based Systems
Text clustering using frequent itemsets
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
A quality driven Hierarchical Data Divisive Soft Clustering for information retrieval
Knowledge-Based Systems
A dissimilarity measure for the k-Modes clustering algorithm
Knowledge-Based Systems
Algorithm for fuzzy clustering of mixed data with numeric and categorical attributes
ICDCIT'05 Proceedings of the Second international conference on Distributed Computing and Internet Technology
A fuzzy k-modes algorithm for clustering categorical data
IEEE Transactions on Fuzzy Systems
Clustering-oriented privacy-preserving data publishing
Knowledge-Based Systems
Fuzzy expert system approach for coronary artery disease screening using clinical parameters
Knowledge-Based Systems
A data mining approach to knowledge discovery from multidimensional cube structures
Knowledge-Based Systems
A sample-based hierarchical adaptive K-means clustering method for large-scale video retrieval
Knowledge-Based Systems
Spatial interaction - modification model and applications to geo-demographic analysis
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
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In many applications, data objects are described by both numeric and categorical features. The k-prototype algorithm is one of the most important algorithms for clustering this type of data. However, this method performs hard partition, which may lead to misclassification for the data objects in the boundaries of regions, and the dissimilarity measure only uses the user-given parameter for adjusting the significance of attribute. In this paper, first, we combine mean and fuzzy centroid to represent the prototype of a cluster, and employ a new measure based on co-occurrence of values to evaluate the dissimilarity between data objects and prototypes of clusters. This measure also takes into account the significance of different attributes towards the clustering process. Then we present our algorithm for clustering mixed data. Finally, the performance of the proposed method is demonstrated by a series of experiments on four real world datasets in comparison with that of traditional clustering algorithms.