Conceptual clustering of structured objects: a goal-oriented approach
Artificial Intelligence
Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Reexamining the cluster hypothesis: scatter/gather on retrieval results
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
A vector space model for automatic indexing
Communications of the ACM
Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values
Data Mining and Knowledge Discovery
IEEE Internet Computing
Cluster-based retrieval using language models
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Data Clustering: Theory, Algorithms, and Applications (ASA-SIAM Series on Statistics and Applied Probability)
Introduction to Information Retrieval
Introduction to Information Retrieval
Cluster Analysis
A survey of Web clustering engines
ACM Computing Surveys (CSUR)
Clustering mixed data based on evidence accumulation
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
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Cluster Analysis consists of the aggregation of data items of a given set into subsets based on some similarity properties. Clustering techniques have been applied in many fields which typically involve a large amount of complex data. This study focuses on what we call multi-domain clustering and labeling, i.e. a set of techniques for multi-dimensional structured mixed data clustering. The work consists of studying the best mix of clustering techniques that address the problem in the multi-domain setting. Considered data types are numerical, categorical and textual. All of them can appear together within the same clustering scenario. We focus on k-means and agglomerative hierarchical clustering methods based on a new distance function we define for this specific setting. The proposed approach has been validated on some real and realistic data-sets based onto college, automobile and leisure fields. Experimental data allowed to evaluate the effectiveness of the different solutions, both for clustering and labeling.