Affiliations: [a] Department of Systems and Computer Networks, Wrocław University of Science and Technology, Wrocław, Poland
| [b] Department of Electrical and Computer Engineering, Queen’s University, Kingston ON, Canada
| [c] Laboratory of Artificial Intelligence and Decision Support and Faculty of Economics, University of Porto, Porto, Portugal
Correspondence:
[*]
Corresponding author. Michał Woźniak, Department of Systems and Computer Networks, Wrocław University of Science and Technology, Wrocław, Poland. E-mail: [email protected].
Abstract: Data stream mining seeks to extract useful information from quickly-arriving, infinitely-sized and evolving data streams. Although these challenges have been addressed throughout the literature, none of them can be considered “solved.” We contribute to closing this gap for the task of data stream clustering by proposing two modifications to the well-known ClusTree data stream clustering algorithm: pruning unused branches and detecting concept drift. Our experimental results show the difficulty in tackling these aspects of data stream mining and the sensitivity of stream mining algorithms to parameter values. We conclude that further research is required to better equip stream learners for the data stream clustering task.
Keywords: Concept drift, data streams, ClusTree
, on-line
learning
