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A novel algorithm which combines clustering analysis and SVM is proposed for classification. Specifically, based on the conglomeration and decentralization characteristics of the positive and negative samples, we present a new type of support vector machine called Clustered Grouping Support Vector Machine or GC-SVM. After clustering training, the samples are divided into different groups, then a series...
Clustering is one of the most important analysis tasks in spatial databases. However, in many real applications, it is more meaningful constrained clustering objects on a spatial network (e.g. road network including traffic information). The existing methods don't refer to the constrained condition. It is therefore difficult to apply them to a real road network. This paper proposes the model of clustering...
Threshold selection is an important topic and also a critical preprocessing step, which directly affects the accuracy of the clustering in a road network. This paper analyzes the necessity of multiple thresholds selection in a road network, extracts the similar nature of the objects, proposes firstly the scheme of multiple thresholds based on support vector regression (SVR) and improves on the existing...
Dynamic cluster organization is a hotspot research field for wireless sensor networks. A new algorithm of building and regroup clusters - improved distributed cluster organization algorithm (IDCOA) is proposed in this paper. By IDCOA, nodes are clustered via the way that some nodes propose invitations, and the other sensors respond. Member nodes in the networks save the cluster's id, but header nodes...
Selecting suitable features is very crucial for achieving successful classification of land cover types. This paper presents a comparative study of three typical feature selection methods for the task of regional land cover classification using MODIS data. Comparison results have shown that Branch and Bound is the best for land cover classification with MODIS data, while ReliefF and mRMR achieve nearly...
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