Identifying Optimal Clusters in Purchase Transaction Data

Clustering in transaction databases can find potentially useful patterns to gain some insight into the structure of the data, which can help for effective decision-making. However, one of the critical tasks in clustering is to identify the appropriate number of clusters, which will determine the performance of any process further applied to the transaction database. This paper presents a methodology to discover the optimal structure of purchase transaction data using the Davies-Bouldin and Calinski-Harabasz validity indices to obtain the number of clusters and formed them with the farthest-first traversals algorithm. The quality of the structures previously formed is evaluated with data complexity measures such as F1, F2, F3, N1 and IR. In this work, we use the support vector machine and multi-layer perceptron classification algorithms, to determine recognition ability in classification problems of more than two classes, and in the context of separability and imbalance of classes present in the groups previously obtained. The experimental results exhibit the viability of the proposed methodology for decision-making.