BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 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
Clustering transactions using large items
Proceedings of the eighth international conference on Information and knowledge management
ACM Computing Surveys (CSUR)
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
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
Interactive Clustering for Transaction Data
DaWaK '01 Proceedings of the Third International Conference on Data Warehousing and Knowledge Discovery
ROCK: A Robust Clustering Algorithm for Categorical Attributes
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
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In this paper, we devise an efficient algorithm for clustering market-basket data items. Market-basket data analysis has been well addressed in mining association rules for discovering the set of large items which are the frequently purchased items among all transactions. In essence, clustering is meant to divide a set of data items into some proper groups in such a way that items in the same group are as similar to one another as possible. In view of the nature of clustering market basket data, we present a measurement, called the small-large (SL) ratio, which is in essence the ratio of the number of small items to that of large items. Clearly, the smaller the SL ratio of a cluster, the more similar to one another the items in the cluster are. Then, by utilizing a self-tuning technique for adaptively tuning the input and output SL ratio thresholds, we develop an efficient clustering algorithm, algorithm STC (standing for Self-Tuning Clustering), for clustering market-basket data. The objective of algorithm STC is "Given a database of transactions, determine a clustering such that the average SL ratio is minimized." We conduct several experiments on the real data and the synthetic workload for performance studies. It is shown by our experimental results that by utilizing the self-tuning technique to adaptively minimize the input and output SL ratio thresholds, algorithm STC performs very well. Specifically, algorithm STC not only incurs an execution time that is significantly smaller than that by prior works but also leads to the clustering results of very good quality.