A Novel Gaussian Based Similarity Measure for Clustering Customer Transactions Using Transaction Sequence Vector

TL;DR

This paper introduces a Gaussian-based similarity measure for clustering customer transactions that considers item distribution and commonality, aiming to improve accuracy over traditional measures like Jaccard or Cosine.

Contribution

The paper proposes a novel Gaussian-based similarity measure that accounts for item distribution and commonality, enhancing transaction clustering and classification accuracy.

Findings

The Gaussian similarity measure outperforms traditional measures in clustering accuracy.

Analysis shows the measure's effectiveness in worst, average, and best case scenarios.

The measure successfully predicts user behavior based on transaction data.

Abstract

Clustering Transactions in sequence, temporal and time series databases is achieving an important attention from the database researchers and software industry. Significant research is carried out towards defining and validating the suitability of new similarity measures for sequence, temporal, time series databases which can accurately and efficiently find the similarity between user transactions in the given database to predict the user behavior. The distribution of items present in the transactions contributes to a great extent in finding the degree of similarity between them. This forms the key idea of the proposed similarity measure. The main objective of the research is to first design the efficient similarity measure which essentially considers the distribution of the items in the item set over the entire transaction data set and also considers the commonality of items present in the transactions, which is the major drawback in the Jaccard, Cosine, Euclidean similarity measures. We then carry out the analysis for worst case, the average case and best case situations. The Similarity measure designed is Gaussian based and preserves the properties of Gaussian function. The proposed similarity measure may be used to both cluster and classify the user transactions and predict the user behaviors.