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Clustering is a fundamental and important technique under many circumstances including data mining, pattern recognition, image processing and other industrial applications. During the past decades, many clustering algorithms have been developed, such as DBSCAN, AP and CFS. As the latest clustering algorithm proposed in Science magazine in 2014, clustering by fast search and find of density peaks,...
In recent years Wireless Sensor Networks have provided a effective solution for sensing and gathering spatial data by ZigBee protocol or other wireless network protocols. So the massive sensor data streams processing has reached many areas of monitoring application in internet of things. The sensor data streams constantly flow in and flow out of the monitoring system, cloud computing can provide a...
Sequential data modeling has received growing interests due to its impact on real world problems. Sequential data is ubiquitous -- financial transactions, advertise conversions and disease evolution are examples of sequential data. A long-standing challenge in sequential data modeling is how to capture the strong hidden correlations among complex features in high volumes. The sparsity and skewness...
Nowadays, a vast ocean of data from different sources is collected, and numerous applications call for the extraction of actionable insights from multi-source data. One important task is to detect untrustworthy information because such information usually indicates critical, unusual, or suspicious activities. The limitation of existing approaches is that they focus on one single source or ignore temporal...
Data stream classification poses many challenges to the data mining community. In this paper, we address four such major challenges, namely, infinite length, concept-drift, concept-evolution, and feature-evolution. Since a data stream is theoretically infinite in length, it is impractical to store and use all the historical data for training. Concept-drift is a common phenomenon in data streams, which...
This paper presents a new approach to using locally linear embedding (LLE) method in object tracking problems. By means of measuring the divergence of the K nearest neighbors of test data, a novel method is proposed to distinguish object from background directly through the LLE embedding results. Avoiding training a mapping function, this approach is less dependent on a beforehand training set of...
Collaborative signal processing cluster-based for target tracking in wireless sensor network is proposed in this paper. Node clustering is a useful approach to reduce the communication overhead and develop data fuse in wireless sensor networks. Each sensor node, which has incomplete information about its dynamic and uncertain world, must respond to sensed events within time constraints. The aim of...
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