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Market-Basket Analysis using Agglomerative Hierarchical approach for clustering a retail items


Rujata Saraf and Sonal Patil


Vol. 16  No. 3  pp. 47-56


With the advent of data mining technology, cluster analysis of items is frequently done in supermarkets and in other large-scale retail sectors. Clustering of items has been a popular tool for identification of different groups of items where appropriate programs and techniques in data mining like Market-Basket analysis have been defined for each group separately with maximum effectiveness and return. For example, items frequently purchased together are placed in one place in the shelf of a retail store. There are various algorithms used for clustering. Hierarchical algorithms find successive clusters using previously established clusters. These algorithms usually are either agglomerative (""bottom-up"") or divisive (""top-down""). The paper presents the Market-Basket Analysis using Agglomerative (“Bottom-up”) hierarchical approach for clustering a retail items. Agglomerative hierarchical clustering creates a hierarchy of clusters which are represented in a tree structure called a Dendorogram. In agglomerative hierarchical clustering, dendrograms are developed based on the concept of ‘distance’ between the entities or, groups of entities. . The clustering will done in such a way that the Purpose of Market-Basket Analysis will achieve.


Market-Basket analysis, Hierarchical Clustering, Agglomerative Hierarchical Clustering, Dendrogram etc…