Adaptive Cluster Based Discovery of High Utility Itemsets

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.