C4.5: programs for machine learning
C4.5: programs for machine learning
Online algorithms for finding profile association rules
Proceedings of the seventh international conference on Information and knowledge management
Clustering transactions using large items
Proceedings of the eighth international conference on Information and knowledge management
Finding generalized projected clusters in high dimensional spaces
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
An Efficient Clustering Algorithm for Market Basket Data Based on Small Large Ratios
COMPSAC '01 Proceedings of the 25th International Computer Software and Applications Conference on Invigorating Software Development
Dimensionality Reduction of Unsupervised Data
ICTAI '97 Proceedings of the 9th International Conference on Tools with Artificial Intelligence
Neural-network feature selector
IEEE Transactions on Neural Networks
Transaction clustering using a seeds based approach
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Rare association rule mining via transaction clustering
AusDM '08 Proceedings of the 7th Australasian Data Mining Conference - Volume 87
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The fast-growing large point of sale databases in stores and companies sets a pressing need for extracting high-level knowledge. Transaction clustering arises to receive attentions in recent years. However, traditional clustering techniques are not useful to solve this problem. Transaction data sets are different from the traditional data sets in their high dimensionality, sparsity and a large number of outliers. In this paper we present and experimentally evaluate a new efficient transaction clustering technique based on cluster of buyers called caucus that can be effectively used for identification of center of cluster. Experiments on real and synthetic data sets indicate that compare to prior work, caucus-based method can derive clusters of better quality as well as reduce the execution time considerably.