Density-based method has emerged as a worthwhile class for clustering data streams. Recently, a number of density-based algorithms have been developed for clustering data streams. However, existing density-based data stream clustering algorithms are not without problem. There is a dramatic decrease in the quality of clustering when there is a range in density of data. In this paper, a new metho...

Data science is currently one of the most promising fields used to support decision-making process. Particularly, data streams can give these supportive systems an updated base knowledge that allows experts make decisions with models. Incremental Decision Rules Algorithm (IDRA) proposes a new incremental decision-rule method based on classical ID3 approach generating and updating rule set. This...

Concept drift and class imbalanced data are major challenging processes involved in modern streaming classification. Particularly, when integrated with difficult factors like the existence of noise, overlapping distribution, concept drift, imbalance can considerably affect classifier results. In addition, various challenges performance existing oversampling schemes such as SMOTE its derivatives...

In this paper we propose a data stream clustering algorithm, called Self Organizing density based clustering over data Stream (SOStream). This algorithm has several novel features. Instead of using a fixed, user defined similarity threshold or a static grid, SOStream detects structure within fast evolving data streams by automatically adapting the threshold for density-based clustering. It also...

Considering the continuity of a data stream, the accessed windows information of a data stream may not be useful as a concept change is effected on further data. In order to support frequent item mining over data stream, the interesting recent concept change of a data stream needs to be identified flexibly. Based on this, an algorithm can be able to identify the range of the further window. A m...