A Novel Clustering Method with Time Interval for Context Inference based on the Multi-sensor Data Fusion

Abstract

Time variation is the essential component of the context awareness. It is a beneficial way not only including time lapse but also clustering time interval for the context inference using the information from sensor mote. In this study, we proposed a novel way of clustering based multi-sensor data fusion for the context inference. In the time interval, we fused the sensed signal of each time slot, and fused again with the results of th first fusion. We could reach the enhanced context inference with assessing the segmented signal according to the time interval at the Dempster-Shafer evidence theory based multi-sensor data fusion.

Enhancement of the k-Means Clustering Speed by Emulation of Birds' Motion in Flock

“…Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar to each other than to those in other groups. It is a main task of common techniques for statistical data analysis, used in many fields including machine learning, pattern recognition, image analysis, information retrieval, bioinformatics, http://dx.doi.org/10.13067/JKIECS.2014.9.9.965 and general signal processing [1][2][3][4][5].…”