Algorithms for clustering data
Algorithms for clustering data
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
An effective hash-based algorithm for mining association rules
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
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
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
Clustering transactions using large items
Proceedings of the eighth international conference on Information and knowledge management
BIRCH: A New Data Clustering Algorithm and Its Applications
Data Mining and Knowledge Discovery
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Efficient and Effective Clustering Methods for Spatial Data Mining
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
An Efficient Algorithm for Mining Association Rules in Large Databases
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
Constraint-Based Rule Mining in Large, Dense Databases
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Integrating recommendation models for improved web page prediction accuracy
ACSC '08 Proceedings of the thirty-first Australasian conference on Computer science - Volume 74
Models for association rules based on clustering and correlation
Intelligent Data Analysis
An integrated model for next page access prediction
International Journal of Knowledge and Web Intelligence
Integration of multiple fuzzy FP-trees
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part I
Using TF-IDF to hide sensitive itemsets
Applied Intelligence
Interestingness measures for association rules within groups
Intelligent Data Analysis
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Mining association rules is one of the most well studied problems in data mining. Current algorithms for finding association rules require several passes over the databases, and obviously the role of I/O overhead is significant for very large databases. In this paper, we present MARC (Mining Association Rules using Clustering), a new algorithm that makes only one full pass over the database. Firstly, we partition the collection of transactions so that similar transactions fall into the same cluster. Then we mine association rules on the summaries of clusters instead of the entire data set. Consequently, a proper method for summarizing a cluster of transactions is proposed. The results of experiments show that the proposed algorithm can learn association rules efficiently in single database pass, and also show that MARC algorithm does not affect too much the accuracy of the association rules learned.