A Soft Set-based Co-occurrence for Clustering Web User Transactions
Grouping web transactions into some clusters are essential to gain a better understanding the behavior of the users, which e-commerce companies widely use this grouping process. Therefore, clustering web transaction is important even though it is challenging data mining issue. The problems arise because there is uncertainty when forming clusters. Clustering web user transaction has used the rough set theory for managing uncertainty in the clustering process. However, it suffers from high computational complexity and low cluster purity. In this study, we propose a soft set-based co-occurrence for clustering web user transactions. Unlike rough set approach that uses similarity approach, the novelty of this approach uses a co-occurrence approach of soft set theory. We compare the proposed approach and rough set approaches regarding computational complexity and cluster purity. The result demonstrates better performance and is more effective so that lower computational complexity is achieved with the improvement more than 100% and cluster purity is higher as compared to two previous rough set-based approaches.
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