Communications of the ACM - Special issue on parallelism
Algorithms for clustering data
Algorithms for clustering data
Symbolic clustering using a new dissimilarity measure
Pattern Recognition
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
PODS '95 Proceedings of the fourteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Inferring Web communities from link topology
Proceedings of the ninth ACM conference on Hypertext and hypermedia : links, objects, time and space---structure in hypermedia systems: links, objects, time and space---structure in hypermedia systems
CACTUS—clustering categorical data using summaries
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values
Data Mining and Knowledge Discovery
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th 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
Similarity-based word sense disambiguation
Computational Linguistics - Special issue on word sense disambiguation
A feature selection technique for classificatory analysis
Pattern Recognition Letters
A k-mean clustering algorithm for mixed numeric and categorical data
Data & Knowledge Engineering
Swarm optimized organizing map (SWOM): A swarm intelligence basedoptimization of self-organizing map
Expert Systems with Applications: An International Journal
Clustering with Domain Value Dissimilarity for Categorical Data
ICDM '09 Proceedings of the 9th Industrial Conference on Advances in Data Mining. Applications and Theoretical Aspects
Context-Based Distance Learning for Categorical Data Clustering
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
Probabilistic self-organizing maps for qualitative data
Neural Networks
Pattern Recognition Letters
Aggregate distance based clustering using fibonacci series-FIBCLUS
APWeb'11 Proceedings of the 13th Asia-Pacific web conference on Web technologies and applications
From Context to Distance: Learning Dissimilarity for Categorical Data Clustering
ACM Transactions on Knowledge Discovery from Data (TKDD)
Clustering and labeling of multi-dimensional mixed structured data
Search Computing
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
Engineering Applications of Artificial Intelligence
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Computation of similarity between categorical data objects in unsupervised learning is an important data mining problem. We propose a method to compute distance between two attribute values of same attribute for unsupervised learning. This approach is based on the fact that similarity of two attribute values is dependent on their relationship with other attributes. Computational cost of this method is linear with respect to number of data objects in data set. To see the effectiveness of our proposed distance measure, we use proposed distance measure with K-mode clustering algorithm to cluster various categorical data sets. Significant improvement in clustering accuracy is observed as compared to clustering results obtained using traditional K-mode clustering algorithm.