In order to solve the modern logistics problem of vehicle distribution, a particle swarm optimization (PSO) algorithm based on clustering analysis is proposed in this paper. This algorithm clusters the target points i...
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ISBN:
(纸本)9780769537788
In order to solve the modern logistics problem of vehicle distribution, a particle swarm optimization (PSO) algorithm based on clustering analysis is proposed in this paper. This algorithm clusters the target points in need of distribution primarily by dbscan algorithm, and then weighted k-means algorithm is used to cluster the target points finally based on the primary clustering. Corresponding vehicles are allocated to every target cluster according to result of clustering analysis, furthermore, path of vehicles are optimized by use of PSO algorithm until all the distribution tasks are finished. Simulation experiments result shows that PSO algorithm based on clustering analysis is feasible and effective in modern logistics distribution process.
In this paper, we propose an efficient algorithm for anomaly detection from call data records. Anomalous users are detected based on fuzzy attribute values derived from their communication patterns. A clustering based...
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ISBN:
(纸本)9783642111631
In this paper, we propose an efficient algorithm for anomaly detection from call data records. Anomalous users are detected based on fuzzy attribute values derived from their communication patterns. A clustering based algorithm is proposed to generate explanations to assist human analysts in validating the results.
The density-based clustering algorithm presented is different from the classical Density-Based Spatial Clustering of Applications with Noise (dbscan) (Ester et al., 1996), and has the following advantages: first, Gree...
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The density-based clustering algorithm presented is different from the classical Density-Based Spatial Clustering of Applications with Noise (dbscan) (Ester et al., 1996), and has the following advantages: first, Greedy algorithm substitutes for R*-tree (Bechmann et al., 1990) in dbscan to index the clustering space so that the clustering time cost is decreased to great extent and I/O memory load is reduced as well; second, the merging condition to approach to arbitrary-shaped clusters is designed carefully so that a single threshold can distinguish correctly all clusters in a large spatial dataset though some density-skewed clusters live in it. Finally, authors investigate a robotic navigation and test two artificial datasets by the proposed algorithm to verify its effectiveness and efficiency.
The density-based clustering algorithm presented is different from the classical Density-Based Spatial Clus- tering of Applications with Noise (dbscan) (Ester et al., 1996), and has the following advantages: first, Gr...
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The density-based clustering algorithm presented is different from the classical Density-Based Spatial Clus- tering of Applications with Noise (dbscan) (Ester et al., 1996), and has the following advantages: first, Greedy algorithm substitutes for R*-tree (Bechmann et al., 1990) in dbscan to index the clustering space so that the clustering time cost is decreased to great extent and I/O memory load is reduced as well; second, the merging condition to approach to arbi- trary-shaped clusters is designed carefully so that a single threshold can distinguish correctly all clusters in a large spatial dataset though some density-skewed clusters live in it. Finally, authors investigate a robotic navigation and test two artificial datasets by the proposed algorithm to verify its effectiveness and efficiency.
The density-based clustering algorithm presented is different from the classical Density-Based Spatial Clustering of Applications with Noise(dbscan)(Ester et al.,1996),and has the following advantages: first,Greedy al...
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The density-based clustering algorithm presented is different from the classical Density-Based Spatial Clustering of Applications with Noise(dbscan)(Ester et al.,1996),and has the following advantages: first,Greedy algorithm substitutes for R*-tree(Bechmann et al.,1990)in dbscan to index the clustering space so that the clustering time cost is decreased to great extent and I/O memory load is reduced as well; second,the merging condition to approach to arbitrary-shaped clusters is designed carefully so that a single threshold can distinguish correctly all clusters in a large spatial dataset though some density-skewed clusters live in it. Finally,authors investigate a robotic navigation and test two artificial datasets by the proposed algorithm to verify its effectiveness and efficiency.
Clustering, in data mining, is a useful technique for discovering interesting data distributions and patterns in the underlying data, and has many application fields, such as statistical data analysis, pattern recogni...
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Clustering, in data mining, is a useful technique for discovering interesting data distributions and patterns in the underlying data, and has many application fields, such as statistical data analysis, pattern recognition, image processing, and etc. We combine sampling technique with dbscan algorithm to cluster large spatial databases, and two sampling based dbscan (Sdbscan) algorithms are developed. One algorithm introduces sampling technique inside dbscan, and the other uses sampling procedure outside dbscan. Experimental results demonstrate that our algorithms are effective and efficient in clustering large scale spatial databases.
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