Algorithms for clustering data
Algorithms for clustering data
Introduction to 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
Reexamining the cluster hypothesis: scatter/gather on retrieval results
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Web document clustering: a feasibility demonstration
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Online association rule mining
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
CACTUS—clustering categorical data using summaries
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
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
Self-Tuning Clustering: An Adaptive Clustering Method for Transaction Data
DaWaK 2000 Proceedings of the 4th International Conference on Data Warehousing and Knowledge Discovery
Discovering Knowledge-Sharing Communities in Question-Answering Forums
ACM Transactions on Knowledge Discovery from Data (TKDD)
A practical approach for clustering transaction data
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
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We propose a clustering algorithm, OAK, targeted to transaction data as typified by market basket data, web documents, and categorical data. OAK is interactive, incremental, and scalable. Use of a dendrogram facilitates the dynamic modification of the number of clusters. In addition, a condensation technique ensures that the dendrogram (regardless of database size) can be memory resident. A performance study shows that the quality of clusters is comparable to ROCK [7] with reduced complexity