There is an increasing awareness for the toxic effects produced by the inhalation of fine particles present in the air. It is important then to provide precise information to the population about the concentrations of...
详细信息
There is an increasing awareness for the toxic effects produced by the inhalation of fine particles present in the air. It is important then to provide precise information to the population about the concentrations of this pollutant expected for the incoming hours. We present here a study about the capability of three types of methods for PM2.5 forecasting one day in advance: a multilayer neural network, a linear algorithm and a clusteringalgorithm. Input variables are past concentrations measured in four monitoring stations and actual and forecasted meteorological information. outputs are the maxima of the 24 h moving average of PM2.5 concentrations for the following day at the site of the monitoring stations, By training with data from the three previous years, we are able to generate results for the fall-winter period for each year from 2004 to 2007. Although the three methods may be used as operational tools, the clusteringalgorithm seems more accurate in detecting high concentration situations. (C) 2008 Elsevier Ltd. All rights reserved.
Unlike K-means,the K-Harmonic means(KHM)is less sensitive to initial ***,KHM as a center-based clusteringalgorithm can only generate a local optimal *** this paper,we develop a new hybrid clustering algorithm combini...
详细信息
Unlike K-means,the K-Harmonic means(KHM)is less sensitive to initial ***,KHM as a center-based clusteringalgorithm can only generate a local optimal *** this paper,we develop a new hybrid clustering algorithm combining Particle Swarm Optimization and K-Harmonic Means(HPSO)for solving this *** algorithm has been implemented and tested on several real *** performance of this algorithm is compared with KHM and *** computational simulations reveal the HPSO clusteringalgorithm combines the ability of global searching of the PSO algorithm and the fast convergence and less sensitive to initial conditions of the KHM *** HPSO is a robust clusteringalgorithm.
This paper presents a new clusteringalgorithm named ANGEL, capable of satisfying various clustering requirements in data mining applications. As a hybrid method that employs discrete-degree and density-attractor, the...
详细信息
ISBN:
(纸本)9783540717003
This paper presents a new clusteringalgorithm named ANGEL, capable of satisfying various clustering requirements in data mining applications. As a hybrid method that employs discrete-degree and density-attractor, the proposed algorithm identifies the main structure of clusters without including the edge of clusters and, then, implements the DBSCAN algorithm to detect the arbitrary edge of the main structure of clusters. Experiment results indicate that the new algorithm accurately recognizes the entire cluster, and efficiently solves the problem of indentation for cluster. Simulation results reveal that the proposed new clusteringalgorithm performs better than some existing well-known approaches such as the K-means, DBSCAN, CLIQUE and GDH methods. Additionally, the proposed algorithm performs very fast and produces much smaller errors than the K-means, DBSCAN, CLIQUE and GDH approaches in most the cases examined herein.
暂无评论