Competitive learning algorithms for vector quantization
Neural Networks
Bayesian classification (AutoClass): theory and results
Advances in knowledge discovery and data mining
An empirical comparison of four initialization methods for the K-Means algorithm
Pattern Recognition Letters
ROCK: a robust clustering algorithm for categorical attributes
Information Systems
Machine Learning and Data Mining; Methods and Applications
Machine Learning and Data Mining; Methods and Applications
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
Interpretable Hierarchical Clustering by Constructing an Unsupervised Decision Tree
IEEE Transactions on Knowledge and Data Engineering
Cluster center initialization algorithm for K-means clustering
Pattern Recognition Letters
CLICKS: Mining Subspace Clusters in Categorical Data via K-Partite Maximal Cliques
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Journal of Classification
IEEE Transactions on Knowledge and Data Engineering
On the Impact of Dissimilarity Measure in k-Modes Clustering Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of kernel and spectral methods for clustering
Pattern Recognition
A new initialization method for categorical data clustering
Expert Systems with Applications: An International Journal
Computation of initial modes for K-modes clustering algorithm using evidence accumulation
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Data clustering: 50 years beyond K-means
Pattern Recognition Letters
Clustering mixed data based on evidence accumulation
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Generalizing self-organizing map for categorical data
IEEE Transactions on Neural Networks
Rival penalized competitive learning for clustering analysis, RBF net, and curve detection
IEEE Transactions on Neural Networks
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Most of the existing clustering approaches are applicable to purely numerical or categorical data only, but not the both. In general, it is a nontrivial task to perform clustering on mixed data composed of numerical and categorical attributes because there exists an awkward gap between the similarity metrics for categorical and numerical data. This paper therefore presents a general clustering framework based on the concept of object-cluster similarity and gives a unified similarity metric which can be simply applied to the data with categorical, numerical, and mixed attributes. Accordingly, an iterative clustering algorithm is developed, whose outstanding performance is experimentally demonstrated on different benchmark data sets. Moreover, to circumvent the difficult selection problem of cluster number, we further develop a penalized competitive learning algorithm within the proposed clustering framework. The embedded competition and penalization mechanisms enable this improved algorithm to determine the number of clusters automatically by gradually eliminating the redundant clusters. The experimental results show the efficacy of the proposed approach.