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Clustering is one of the most widely used techniques for exploratory data analysis. Across all disciplines, from social sciences over biology to computer science, people try to get a first intuition about their data by identifying meaningful groups among the data objects. K-means is one of the most famous clustering algorithms. Its simplicity and speed allow it to run on large data sets. However,...
Traditional clustering algorithms for WSN (wireless sensor networks) select only one CH (cluster head) in each cluster, which consumes energy at the CH fast and shortens the network lifetime greatly. In this paper, EBCMS (energy-balanced clustering algorithm with master/slave method) is proposed to solve this problem. The key idea of the algorithm is that one master CH and two slave CHs are chosen...
In this paper, we propose a new intrusion detection technology which combines feature extraction with wavelet clustering method. Our intrusion detection model setup has two phases, where the first phase is to project the input data into high dimensional space by using the discriminant vectors extracted by Kernel Fisher Discriminant Analysis. By using KFDA, we can reduce the dimension of the input...
In recent years, there has been an increasing interest in data clustering of short documents. Existing works consider seldom the concept similarity between the words, so the quality of clustering is often very low. This paper proposes a new document-clustering algorithm based on concept similarity in Chinese text processing. Different from tradition method, the algorithm converts text into a words...
Automatic finger classification is an important part of fingerprint automatic identification system (FAIS). Its function is to provide a search system for large size database. Accurate classification can reduce searching time and expediate matching speed. Support vector machine (SVM) is a new learning technique based on statistical learning theory (SLT). SVM was originally developed for two-class...
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