TY - CONF T1 - Clustering Individual Transactional Data for Masses of Users T2 - Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining Y1 - 2017 A1 - Riccardo Guidotti A1 - Anna Monreale A1 - Mirco Nanni A1 - Fosca Giannotti A1 - Dino Pedreschi AB - Mining a large number of datasets recording human activities for making sense of individual data is the key enabler of a new wave of personalized knowledge-based services. In this paper we focus on the problem of clustering individual transactional data for a large mass of users. Transactional data is a very pervasive kind of information that is collected by several services, often involving huge pools of users. We propose txmeans, a parameter-free clustering algorithm able to efficiently partitioning transactional data in a completely automatic way. Txmeans is designed for the case where clustering must be applied on a massive number of different datasets, for instance when a large set of users need to be analyzed individually and each of them has generated a long history of transactions. A deep experimentation on both real and synthetic datasets shows the practical effectiveness of txmeans for the mass clustering of different personal datasets, and suggests that txmeans outperforms existing methods in terms of quality and efficiency. Finally, we present a personal cart assistant application based on txmeans JF - Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining PB - ACM ER -