Data stream mining has attracted much research attention from the data mining community. With the advance of wireless networks and mobile devices, the concept of ubiquitous data mining has been proposed. However, mobile devices are resource-constrained, which makes data stream mining a greater challenge. In this paper, we propose the RA-HCluster algorithm that can be used in mobile devices for ...

Knowledge embedded in a data stream is likely to be changed as time goes by. Identifying the recent change of the knowledge quickly can provide valuable information for the analysis of the data stream. However, most mining algorithms over a data stream are not able to extract the recent change of knowledge in a data stream adaptively. This is because the obsolete information of old data element...

A growing number of applications that generate massive streams of data need intelligent data processing and online analysis. Real-time surveillance systems, telecommunication systems, sensor networks and other dynamic environments are such examples. The imminent need for turning such data into useful information and knowledge augments the development of systems, algorithms and frameworks that a...

the closed nature of geochemical data has been proven in many studies. compositional data have special properties that mean that standard statistical methods cannot be used to analyse them. these data imply a particular geometry called aitchison geometry in the simplex space. for analysis, the dataset must first be opened by the various transformations provided. one of the most popular of the a...

As a kind of stream data mining method, stream clustering has great potentiality in areas such as network traffic analysis, intrusion detection, etc. This paper proposes a novel grid-based clustering algorithm for stream data, which has both advantages of grid mapping and DBSCAN algorithm. The algorithm adopts the two-phase model and in the online phase, it maps stream data into a grid and the ...

Frequent pattern discovery over data stream is a hard problem because a continuously generated nature of stream does not allow a revisit on each data element. Furthermore, pattern discovery process must be fast to produce timely results. Based on these requirements, we propose an approximate approach to tackle the problem of discovering frequent patterns over continuous stream. Our approximatio...

A simple existing data stream clustering algorithm DenStream based on DBScan is studied. Based on DenStream a modified algorithm called DenStream2 is proposed. It follows most of the framework and theory of DenStream. Denstream2 is implemented as a foreign function in an extensible data stream management system (DSMS), where queries over streams are allowed. The generated clusters inferred from...

-Classification and analysis of data streams are the most promising fields of research and development in Data stream mining. Ensemble based classification approach is one the most challenging flavor of developing an efficient classifier due to large number available base classifiers and increase in the computational time required for training and classification. This research emphasizes on dev...