Algorithms for clustering data
Algorithms for clustering data
Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
CACTUS—clustering categorical data using summaries
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Finding generalized projected clusters in high dimensional spaces
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
COOLCAT: an entropy-based algorithm for categorical clustering
Proceedings of the eleventh international conference on Information and knowledge management
Unsupervised Learning with Mixed Numeric and Nominal Data
IEEE Transactions on Knowledge and Data Engineering
ROCK: A Robust Clustering Algorithm for Categorical Attributes
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
SCHISM: A New Approach for Interesting Subspace Mining
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Automated Variable Weighting in k-Means Type Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clicks: An effective algorithm for mining subspace clusters in categorical datasets
Data & Knowledge Engineering
An Entropy Weighting k-Means Algorithm for Subspace Clustering of High-Dimensional Sparse Data
IEEE Transactions on Knowledge and Data Engineering
A k-mean clustering algorithm for mixed numeric and categorical data
Data & Knowledge Engineering
On Data Labeling for Clustering Categorical Data
IEEE Transactions on Knowledge and Data Engineering
Reducing Redundancy in Subspace Clustering
IEEE Transactions on Knowledge and Data Engineering
Subspace and projected clustering: experimental evaluation and analysis
Knowledge and Information Systems
Density Conscious Subspace Clustering for High-Dimensional Data
IEEE Transactions on Knowledge and Data Engineering
An architecture for component-based design of representative-based clustering algorithms
Data & Knowledge Engineering
Dynamic clustering of histogram data based on adaptive squared Wasserstein distances
Expert Systems with Applications: An International Journal
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Almost all subspace clustering algorithms proposed so far are designed for numeric datasets. In this paper, we present a k-means type clustering algorithm that finds clusters in data subspaces in mixed numeric and categorical datasets. In this method, we compute attributes contribution to different clusters. We propose a new cost function for a k-means type algorithm. One of the advantages of this algorithm is its complexity which is linear with respect to the number of the data points. This algorithm is also useful in describing the cluster formation in terms of attributes contribution to different clusters. The algorithm is tested on various synthetic and real datasets to show its effectiveness. The clustering results are explained by using attributes weights in the clusters. The clustering results are also compared with published results.