<![CDATA[Market basket analysis is a generic term for methodology that study the composition of a basket of products. It has the objective of indentifying products, or groups of products, that tend to occur together (are associated). The discovery of this relationship can help merchant to develop a strategy of sales to consider the goods are often purchased with by customer. The knowledge that obtained market analysis basket is very important, because it can help recommendations product and promotion products so marketing strategy to be more appropriate. Market basket analysis can approach with Association Rule, such as apriori and FP-Growth. But they are a number of technical issues relating to the most common recommendations techniques. Association Rule tend to ignore the large itemset, To overcome these problems, existing attributes clustered to form groups of the same attributes and then determine the association patterns in each group. This study will use CLARANS algorithm for clustering on sales data and apply the FP-Growth algorithm to approach the association in each cluster. So that the product recommendations to customers to be more accurate because the Dataset that will be associated to be smaller. To the experimentally determined value of Minimum Support is 70% - 100% and Confidence Minimum value 70% - 100%. From the measurement results using Support, Confidence and Lift Ratio isfound that a high number of rule in third cluster.]]>
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