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Clustering techniques have gained great popularity in neuroscience data analysis especially in analysing data from complex experiment paradigm where it is hard to apply traditional model-based method. However, when employing clustering analysis, many clustering algorithms are available nowadays and even with an individual clustering algorithm, choices like parameter settings and distance metrics are...
In this paper, we propose a scalable clustering paradigm to address the problems of excessive computational load and limited clustering performance in large-scale data. The proposed method employs the enhanced splitting merging awareness tactics (E-SMART) algorithm. The large-scale dataset is divided into many sub-datasets sampled randomly from original data. These sub-datasets are clustered using...
Affinity propagation clustering algorithm is with a broad value in science and engineering because of it no need to input the number of clusters in advances, robustness and good generalization. But the algorithm needs the initial similarity (the distance between any two points) as a parameter, a lot of time and storage space is required for the calculation of similarity. It's limited to apply to cluster...
For monitoring burst events in reactive wireless sensor networks (WSNs),an novel energy-efficient dynamic voting cluster (EE-DVC) algorithm is proposed. EE-DVC has three obvious features: Firstly, it adopts virtual grid ideas to divide each cluster into MtimesN square area and select a working node in each grid to reduce redundant information and save energy. Secondly, cluster head is dynamic voted...
With tremendous and ever-growing amounts of electronic documents from World Wide Web and digital libraries, it becomes more and more difficult to get information that people really want. In order to predigest search process, people use clustering method to browse through search results. However traditional Chinese information clustering techniques are inadequate since they don't generate clusters...
Because of today's explosive information from Internet, people will contact much new information at any moment. So how to analyze this non-stationary information becomes more and more important. Clustering analysis is a good information analysis method, but many clustering algorithms only fit to stationary situation. Then in this paper, a novel incremental clustering algorithm based on self-organizing-mapping-IGSOM...
In order to reduce dimension number of feature space and improve clustering precision, a novel SOM clustering algorithm based on feature selection-FSSOM is provided in this paper. This algorithm first evaluates importance and distinguishing ability of each feature, and only selects features which can efficiently improve clustering precision to construct feature space. Then, it computes kullback-leibler...
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