In many applications, classifiers need to be built based on multiple related data streams. For example, stock streams and news streams are related, where the classification patter...
Yabo Xu, Ke Wang, Ada Wai-Chee Fu, Rong She, Jian ...
Clustering is to identify densely populated subgroups in data, while correlation analysis is to find the dependency between the attributes of the data set. In this paper, we combin...
Detecting the failure of a data stream is relatively easy when the stream is continually full of data. The transfer of large amounts of data allows for the simple detection of inte...
Registration (or alignment) of the synthetic imagery with the real world is crucial in augmented reality (AR) systems. The data from user-input devices, tracking devices, and imag...
There is growing interest in run-time detection as parallel and distributed systems grow larger and more complex. This work targets run-time analysis of complex, interactive scien...
Data-intensive, interactive applications are an important class of metacomputing (Grid) applications. They are characterized by large, time-varying data flows between data provid...
The data stream model has recently attracted attention for its applicability to numerous types of data, including telephone records, web documents and clickstreams. For analysis o...
An increasing number of computer vision applications require on-line processing of data streams, preferably in real-time. This trend is fueled by the mainstream availability of low...
Abstract. Mining interesting patterns in data streams has attracted special attention recently. This study revealed the principles behind observations, through variation of interve...
Yue Wang, Changjie Tang, Chuan Li, Yu Chen, Ning Y...
We propose two new improvements for bagging methods on evolving data streams. Recently, two new variants of Bagging were proposed: ADWIN Bagging and Adaptive-Size Hoeffding Tree (...
Albert Bifet, Geoffrey Holmes, Bernhard Pfahringer...