<![CDATA[Market basket analysis seeks to apply association rule mining on the massive sales transaction data. It yields an outcome that either aims to suppress product stock-up unnecessarily and/or product being stock-out. Such decision support system seeks to avoid the unnecessary demurrage and help businesses to keep their customers via better decision and improved service. Market data are time-bound on supply-demand value chain. With customer behavior varying in time, we seek to predict purchase of commonly combined itemset for a next period ? so that businesses can better support their decisions via adequate provisions of the required inventory. We use 3-KDD dataset and Delta Mall dataset ? adapting a time-clustering algorithm that examines buying behavior of customers, their preferences and frequency with which goods are purchased in common as a basket. Model yields average 162-rules for four-dataset from dataset. Result shows that previous basket items by random customers allow the selection purchase of items of similar value as best combined due to its shelf-placement using the concept of feature drift.]]>
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