Analyzing Association Rule Mining and Clustering on Sales Day Data with XLMiner and Weka

In the era of intense competition among organizations, retaining a customer is a collaborative process. Business organizations are adopting different strategies to facilitate their customers in verity of ways, so that these customers keep on buying from them. Association Rule Mining (ARM) is one of the strategies that find out correspondence/association among the items sold together by applying basket analysis. The clustering technique is also used for different advantages like; recognizing class of most sold products, classifying customers based on their buying behavior and their power of purchase. Different researchers have provided different algorithms for both ARM and Clustering, and are implemented in different data mining tools. This paper is extended version of [4], we have compared the results of Apriori and K-Mean algorithms against their implementation in Weka and XLMiner. For this comparison we have used the transaction data of Sales Day (a super store). The results are very encouraging and also produced valuable information for sales and business improvements. We have also analyzed the data for hidden knowledge and the results showed some very interesting patterns in user buying behavior and buying timings.