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We develop and design a novel clustering algorithm to capture utility information in transactional data. Transactional data is a special type of categorical data where transactions can be of varying length. A key objective for all categorical data analysis is pattern recognition. Therefore, transactional clustering algorithms focus on capturing the information on high frequency patterns from the data in the clusters. In recent times, utility information for category types in the data has been added to the transactional data model for a more realistic representation of data. As a result, the key information of interest has become high utility patterns instead of high frequency patterns. To the best our knowledge, no existing clustering algorithm for transactional data captures the utility information in the clusters found. Along with our new clustering rationale we also develop corresponding metrics for evaluating quality of clusters found. Experiments on real datasets show that the clusters found by our algorithm successfully capture the high utility patterns in the data. Comparative experiments with other clustering algorithms further illustrate the effectiveness of our algorithm.

A Novel Clustering Algorithm to Capture Utility Information in Transactional Data