CACTUS—clustering categorical data using summaries
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Pattern Detection and Discovery
Proceedings of the ESF Exploratory Workshop on Pattern Detection and Discovery
TRICLUSTER: an effective algorithm for mining coherent clusters in 3D microarray data
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Mining Approximate Frequent Itemsets from Noisy Data
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Clicks: An effective algorithm for mining subspace clusters in categorical datasets
Data & Knowledge Engineering
Simultaneous Pattern and Data Clustering for Pattern Cluster Analysis
IEEE Transactions on Knowledge and Data Engineering
Quantitative evaluation of approximate frequent pattern mining algorithms
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Closed patterns meet n-ary relations
ACM Transactions on Knowledge Discovery from Data (TKDD)
Closed and noise-tolerant patterns in n-ary relations
Data Mining and Knowledge Discovery
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To increase the relevancy of local patterns discovered from noisy relations, it makes sense to formalize error-tolerance. Our starting point is to address the limitations of state-of-the-art methods for this purpose. Some extractors perform an exhaustive search w.r.t. a declarative specification of error-tolerance. Nevertheless, their computational complexity prevents the discovery of large relevant patterns. Alpha is a 3-step method that (1) computes complete collections of closed patterns, possibly error-tolerant ones, from arbitrary n-ary relations, (2) enlarges them by hierarchical agglomeration, and (3) selects the relevant agglomerated patterns.