Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 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
Algorithms for association rule mining — a general survey and comparison
ACM SIGKDD Explorations Newsletter
Mining optimized support rules for numeric attributes
Information Systems
Discovering local structure in gene expression data: the order-preserving submatrix problem
Proceedings of the sixth annual international conference on Computational biology
Biclustering of Expression Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Parallel coordinates: a tool for visualizing multi-dimensional geometry
VIS '90 Proceedings of the 1st conference on Visualization '90
Clustering of Time Series Subsequences is Meaningless: Implications for Previous and Future Research
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Cluster Analysis for Gene Expression Data: A Survey
IEEE Transactions on Knowledge and Data Engineering
Linear correlation discovery in databases: a data mining approach
Data & Knowledge Engineering
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Automatic Subspace Clustering of High Dimensional Data
Data Mining and Knowledge Discovery
Clustering short time series gene expression data
Bioinformatics
A knowledge-driven approach to cluster validity assessment
Bioinformatics
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Discovering significant OPSM subspace clusters in massive gene expression data
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Effective similarity measures for expression profiles
Bioinformatics
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Making clustering in delay-vector space meaningful
Knowledge and Information Systems
Mining gene–sample–time microarray data: a coherent gene cluster discovery approach
Knowledge and Information Systems
Pattern-based time-series subsequence clustering using radial distribution functions
Knowledge and Information Systems
Why does subsequence time-series clustering produce sine waves?
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Permutation tests for classification
COLT'05 Proceedings of the 18th annual conference on Learning Theory
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The representation of multiple continuous attributes as dimensions in a vector space has been among the most influential concepts in machine learning and data mining. We consider sets of related continuous attributes as vector data and search for patterns that relate a vector attribute to one or more items. The presence of an item set defines a subset of vectors that may or may not show unexpected density fluctuations. We test for fluctuations by studying density histograms. A vector–item pattern is considered significant if its density histogram significantly differs from what is expected for a random subset of transactions. Using two different density measures, we evaluate the algorithm on two real data sets and one that was artificially constructed from time series data.