Algorithms for clustering data
Algorithms for clustering data
Data mining and knowledge discovery in databases
Communications of the ACM
CACTUS—clustering categorical data using summaries
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
ACM Computing Surveys (CSUR)
A robust and scalable clustering algorithm for mixed type attributes in large database environment
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
A discrete-valued clustering algorithm with applications to biomolecular data
Information Sciences: an International Journal
Data Mining: Introductory and Advanced Topics
Data Mining: Introductory and Advanced Topics
Data Mining: Concepts, Models, Methods and Algorithms
Data Mining: Concepts, Models, Methods and Algorithms
COOLCAT: an entropy-based algorithm for categorical clustering
Proceedings of the eleventh international conference on Information and knowledge management
BIRCH: A New Data Clustering Algorithm and Its Applications
Data Mining and Knowledge Discovery
Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values
Data Mining and Knowledge Discovery
Squeezer: an efficient algorithm for clustering categorical data
Journal of Computer Science and Technology
Unsupervised Learning with Mixed Numeric and Nominal Data
IEEE Transactions on Knowledge and Data Engineering
Data-Driven Discovery of Quantitative Rules in Relational Databases
IEEE Transactions on Knowledge and Data Engineering
Clustering Categorical Data: An Approach Based on Dynamical Systems
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
ROCK: A Robust Clustering Algorithm for Categorical Attributes
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
GeneScout: a data mining system for predicting vertebrate genes in genomic DNA sequences
Information Sciences: an International Journal - Special issue: Soft computing data mining
Fuzzy clustering of categorical data using fuzzy centroids
Pattern Recognition Letters
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Temporal analysis of clusters of supermarket customers: conventional versus interval set approach
Information Sciences—Informatics and Computer Science: An International Journal
Looking into the seeds of time: Discovering temporal patterns in large transaction sets
Information Sciences: an International Journal
Electricity based external similarity of categorical attributes
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
DECA: A Discrete-Valued Data Clustering Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modified adaptive resonance theory network for mixed data based on distance hierarchy
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part IV
A fuzzy k-modes algorithm for clustering categorical data
IEEE Transactions on Fuzzy Systems
Generalizing self-organizing map for categorical data
IEEE Transactions on Neural Networks
Mining typical patterns from databases
Information Sciences: an International Journal
EED: Energy Efficient Disk drive architecture
Information Sciences: an International Journal
Information Sciences: an International Journal
Exploiting the performance gains of modern disk drives by enhancing data locality
Information Sciences: an International Journal
A new point symmetry based fuzzy genetic clustering technique for automatic evolution of clusters
Information Sciences: an International Journal
Towards supporting expert evaluation of clustering results using a data mining process model
Information Sciences: an International Journal
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
Pairwise-adaptive dissimilarity measure for document clustering
Information Sciences: an International Journal
Automatic threshold estimation for data matching applications
Information Sciences: an International Journal
Minimum spanning tree based split-and-merge: A hierarchical clustering method
Information Sciences: an International Journal
A dissimilarity measure for the k-Modes clustering algorithm
Knowledge-Based Systems
Determining the number of clusters using information entropy for mixed data
Pattern Recognition
Learning data structure from classes: A case study applied to population genetics
Information Sciences: an International Journal
DBCAMM: A novel density based clustering algorithm via using the Mahalanobis metric
Applied Soft Computing
Adjusting the clustering results referencing an external set
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part II
Adapting domain ontology for personalized knowledge search and recommendation
Information and Management
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Data clustering is an important data mining technique which partitions data according to some similarity criterion. Abundant algorithms have been proposed for clustering numerical data and some recent research tackles the problem of clustering categorical or mixed data. Unlike the subtraction scheme used for numerical attributes, there is no standard for measuring distance between categorical values. In this article, we propose a distance representation scheme, distance hierarchy, which facilitates expressing the similarity between categorical values and also unifies distance measuring of numerical and categorical values. We then apply the scheme to mixed data clustering, in particular, to integrate with a hierarchical clustering algorithm. Consequently, this integrated approach can uniformly handle numerical data and categorical data, and also enables one to take the similarity between categorical values into consideration. Experimental results show that the proposed approach produces better clustering results than conventional clustering algorithms when categorical attributes are present and their values have different degree of similarity.