Scatter/Gather: a cluster-based approach to browsing large document collections
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Applied multivariate techniques
Applied multivariate techniques
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
ACM Computing Surveys (CSUR)
ROCK: a robust clustering algorithm for categorical attributes
Information Systems
Clustering Categorical Data: An Approach Based on Dynamical Systems
VLDB '98 Proceedings of the 24rd 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
A new clustering algorithm for transaction data via caucus
PAKDD'03 Proceedings of the 7th 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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Transaction clustering has received a great deal of attention in the past few years. Its functionality extends well beyond traditional clustering algorithms which basically perform a near-neighbourhood search for locating groups of similar instances. The basic concept underlying transaction clustering stems from the concept of large items as defined by association rule mining algorithms. Clusters formed on the basis of large items that are shared between instances offer an attractive alternative to association rule mining systems. Currently, none of the techniques proposed offer a good solution to scenarios where large items overlap across clusters. In this paper we overcome the aforementioned limitations by using cluster seeds that represent initial centroids. Seeds are generated from sets of transaction items that occur together above a certain threshold and such seeds may overlap in their itemsets across clusters.