Existing density-based data stream clustering algorithms use a two-phase scheme approach consisting of an online phase, in which raw data is processed to gather summary statistics...
Agostino Forestiero, Clara Pizzuti, Giandomenico S...
Abstract. Novelty detection in data stream mining denotes the identification of new or unknown situations in a stream of data elements flowing continuously in at rapid rate. This...
Abstract--Data clustering is a highly used knowledge extraction technique and is applied in more and more application domains. Over the last years, a lot of algorithms have been pr...
In this paper, we present new approaches to handling drift and shift in on-line data streams with the help of evolving fuzzy systems (EFS), which are characterized by the fact tha...
Abstract. This paper proposes a general framework for classifying data streams by exploiting incremental clustering in order to dynamically build and update an ensemble of incremen...
Ioannis Katakis, Grigorios Tsoumakas, Ioannis P. V...