Combinatorica
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Neural computation and self-organizing maps: an introduction
Neural computation and self-organizing maps: an introduction
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Coloring random and semi-random k-colorable graphs
Journal of Algorithms
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
The Markov chain Monte Carlo method: an approach to approximate counting and integration
Approximation algorithms for NP-hard problems
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Fast discovery of association rules
Advances in knowledge discovery and data mining
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Searching Multimedia Databases by Content
Searching Multimedia Databases by Content
Statistical Language Learning
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Sampling Large Databases for Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Spectral partitioning works: planar graphs and finite element meshes
FOCS '96 Proceedings of the 37th Annual Symposium on Foundations of Computer Science
Studying Recommendation Algorithms by Graph Analysis
Journal of Intelligent Information Systems
Algebraic Techniques for Analysis of Large Discrete-Valued Datasets
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
A New Conceptual Clustering Framework
Machine Learning
Organizing structured web sources by query schemas: a clustering approach
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Compression, Clustering, and Pattern Discovery in Very High-Dimensional Discrete-Attribute Data Sets
IEEE Transactions on Knowledge and Data Engineering
A database clustering methodology and tool
Information Sciences—Informatics and Computer Science: An International Journal
Categorical data visualization and clustering using subjective factors
Data & Knowledge Engineering
Core algorithms in the CLEVER system
ACM Transactions on Internet Technology (TOIT)
MMR: An algorithm for clustering categorical data using Rough Set Theory
Data & Knowledge Engineering
Top-Down Parameter-Free Clustering of High-Dimensional Categorical Data
IEEE Transactions on Knowledge and Data Engineering
Learning decision trees with taxonomy of propositionalized attributes
Pattern Recognition
Spectral Embedding of Feature Hypergraphs
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Multiobjective genetic algorithm-based fuzzy clustering of categorical attributes
IEEE Transactions on Evolutionary Computation
A rough set approach for selecting clustering attribute
Knowledge-Based Systems
Electricity based external similarity of categorical attributes
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
A polynomial characterization of hypergraphs using the Ihara zeta function
Pattern Recognition
Propositionalized attribute taxonomies from data for data-driven construction of concise classifiers
Expert Systems with Applications: An International Journal
Aggregate distance based clustering using fibonacci series-FIBCLUS
APWeb'11 Proceedings of the 13th Asia-Pacific web conference on Web technologies and applications
INCONCO: interpretable clustering of numerical and categorical objects
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
DISC: data-intensive similarity measure for categorical data
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
Multinomial event model based abstraction for sequence and text classification
SARA'05 Proceedings of the 6th international conference on Abstraction, Reformulation and Approximation
Clustering structured web sources: a schema-based, model-differentiation approach
EDBT'04 Proceedings of the 2004 international conference on Current Trends in Database Technology
Clustering of heterogeneously typed data with soft computing - a case study
MICAI'11 Proceedings of the 10th international conference on Artificial Intelligence: advances in Soft Computing - Volume Part II
Hypergraph based information-theoretic feature selection
Pattern Recognition Letters
MAR: Maximum Attribute Relative of soft set for clustering attribute selection
Knowledge-Based Systems
iHypR: Prominence ranking in networks of collaborations with hyperedges1
ACM Transactions on Knowledge Discovery from Data (TKDD)
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We describe a novel approach for clustering collections of sets, and its application to the analysis and mining of categorical data. By “categorical data,” we mean tables with fields that cannot be naturally ordered by a metric – e.g., the names of producers of automobiles, or the names of products offered by a manufacturer. Our approach is based on an iterative method for assigning and propagating weights on the categorical values in a table; this facilitates a type of similarity measure arising from the co-occurrence of values in the dataset. Our techniques can be studied analytically in terms of certain types of non-linear dynamical systems.