01663nas a2200157   4500008004100000245006500041210006500106260000800171520113100179100002301310700001901333700001701352700002101369700002001390856009501410       2017                        eng d00aClustering Individual Transactional Data for Masses of Users0 aClustering Individual Transactional Data for Masses of Users  bACM3 aMining 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 txmeans1 aGuidotti, Riccardo1 aMonreale, Anna1 aNanni, Mirco1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/clustering-individual-transactional-data-masses-users