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Clustering is a method of unsupervised learning, and a common technique for statistical data analysis used in many fields, including machine learning, data mining, pattern recognition, image analysis and bioinformatics a novel algorithm based on clustering to extract rules from neural networks is proposed. After neural networks have been trained and pruned successfully, inner-rules are generated by...
Representation and similarity measure of time series is the research basic of the time-series data mining. This paper uses ESAX (extended symbolic aggregate approximation) representing the time series similarly and raises an improved time series method of similarity measure ESSVS (ESAX statistical vector space) based on the statistics symbolic vector space method. ESSVS measure the time series similarity...
Existing density-based data stream clustering algorithms use a two-phase scheme approach consisting of an online phase, in which raw data is processed to gather summary statistics, and an offline phase that generates the clusters by using the summary data. In this paper we propose a data stream clustering method based on a multi-agent system that uses a decentralized bottom-up self-organizing strategy...
Panel data is a two-dimensional data included time and the cross section. Through constructing a panel data matrix, the clustering method is applied to panel data analysis. This method solves the heterogeneity question of dependent variable which belongs to panel data, before on their statistical analysis. In the part of case analysis, the question about discount rate of B stock in China is studied...
For the evaluation of indoor thermal comfort, the assessment for main factors, such as air temperature, velocity, relative humidity and mean radiation temperature, are independent and incompatible. In this paper, PPE model was established for thermal comfort evaluation, and also GA was selected to optimize the projection direction. Therefore multi-dimension data could be changed into low dimension...
There are two kinds of data collection in wireless sensor network: passive and active. We here propose an adaptive, energy-efficient clustering protocol (AEEC) which is applicable to both passive and active data collection. The proposed protocol selects cluster heads according to the rank of node remaining energy and the number of node neighbors. Unlike traditional clustering protocols, it is an adaptive...
Clustering description problem is one of key issues of the traditional document clustering algorithm. The traditional document algorithm can cluster the objects, but it can not give concept description for the clustered results. Document clustering description is a problem of labeling the clustered results of document collection clustering. It can help users determine whether one of the clusters is...
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