In this paper, we propose a new research problem on active learning from data streams where data volumes grow continuously. The objective is to label a small portion of stream data from which a model is derived to predict future instances as accurately as possible. We propose a classifier-ensemble based active learning framework which selectively labels instances from data streams to build an e...

Data streams are ordered sets of values that are fast, continuous, mutable, and potentially unbounded. Examples of data streams include the pervasive time series which span domains such as finance, medicine, and transportation. Mining data streams require approaches that are efficient, adaptive, and scalable. For several stream mining tasks, knowledge of the data’s probability density function ...

In this paper, a novel method for analyzing time-series data and extracting time-correlations among multiple time-series data streams is described. The time-correlations tell us the relationships and dependencies among time-series data streams. Reusable time-correlation rules can be fed into various analysis tools, such as forecasting or simulation tools, for further analysis. Statistical techn...

It presents some definitions of projected cluster and projected cluster group on hybrid attributes after having given some definitions on ordered attributes and sorted attributes to solve clustering analysis problem of infinite hybrid attributes data streams in finite space. In order to improve the clustering quality of hybrid attributes data streams, it presents a two-step projected clustering...

Graph streams have been extensively studied, for instance, for data mining and for stream reasoning, while a fairly limited number of studies have been conducted on visualizations. Recently, edge bundling methods has been extensively investigated to reduce visual clutter in large graph visualizations, however, have focused on static graphs. This paper presents a new framework, namely StreamEB, ...

The importance of leaf litter to streams is well known, as is the series of events involved in leaf decay (leaf processing). What is currently missing, however, is an understanding of how the numerous, interacting variables controlling leaf-processing rates in streams can be organized. We suggest that leaf processing is scale-dependent and that factors controlling processing rates will largely ...