# Adaptive Cluster Based Discovery of High Utility Itemsets

## Abstract

Utility Itemset Mining (UIM) is a key analysis technique for data which is modeled by the Transactional data model. While improving the computational time and space efficiency of the mining of itemsets is important, it is also critically important to predict future itemsets accurately. In today’s time, when both scientific and business competitive edge is commonly derived from first access to knowledge via advanced predictive ability, this problem becomes increasingly relevant. We established in our most recent work that having prior knowledge of approximate cluster structure of the dataset and using it implicitly in the mining process, can lend itself to accurate prediction of future itemsets. We evaluate the individual strength of each transaction while focusing on itemset prediction, and reshape the transaction utilities based on that. We extend our work by identifying that such reshaping of transaction utilities should be adaptive to the anticipated cluster structure, if there is a specific intended prediction window. We define novel concepts for making such an anticipation and integrate Time Series Forecasting into the evaluation. We perform additional illustrative experiments to demonstrate the application of our improved technique and also discuss future direction for this work.

## Keywords

High utility itemset mining Clustering Adaptive itemset prediction Time Series forecasting## Notes

### Acknowledgements

The research reported in this paper is funded in part by Philip and Virginia Sproul Professorship Endowment and Jerry R. Junkins Endowments at Iowa State University. The research computation is supported by the HPC@ISU equipment at Iowa State University, some of which has been purchased through funding provided by NSF under MRI grant number CNS 1229081 and CRI grant number 1205413. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the funding agencies. We also thank Dr. Mayank Mishra, former Post Doctoral Fellow at Iowa State University for his contributions, suggestions and feedback during the intial phase of this work. He was also co-author on the conference paper [14] of which this work is an extension.

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