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

Abstract

Abstract Clustering transactions in sequence databases, temporal databases, and time series databases is achieving an important attention from the database researchers. There is a significant research being carried towards defining and validating the suitability of new similarity measures for sequence databases, temporal databases, time series databases which can accurately and efficiently find the similarity between any two given user transactions in the database of transactions 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 for the design of the proposed similarity measure. The main objective of this research is to design similarity function to find similarity between two user transactions by defining two terms called transaction sequence vector and transaction vector and use them for defining the proposed measure. We then carry out the analysis for worst case, average case and best case situations. The Similarity measure designed is Gaussian based and preserves the properties of Gaussian function.