Heuristic for context detection in time varying domains

Abstract

The primary goal is to find information of co-existing commodities called itemsets in transactional databases. Especially in business to make a proper decision, the knowledge of high support itemset is very important. For example: A business can avoid giving discounts on an item which is more in demand though it is one of the commodities of the same itemset. This phenomenal product’s information must be known to data analyst. As we see the change becomes mandatory as the season changes. So it has a great effect on buying habits of customers, not only on the season but also newly introduced merchandise [2, 5, 6]. The primary duty of data analyst is to detect these changes i,e which high-support itemsets withstand the change and which itemset among itself vanishes and which new itemsets emerge. To take this challenge we use a window of the latest market-basket which shows variation in its size time to time. The window grows in the periods of stability, producing an information of the current context. The window reduces in size, once the change is detected. The main objective of this paper is to introduce a new operator for controlling the window size.