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Improved DBSCAN algorithm for bus station clustering

2013-09-22
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This paper proposes an improved DBSCAN algorithm suitable for bus station clustering, which reduces the search radius ε, thereby improving the clustering accuracy. At the same time, it determines the merging of connected clusters through shared objects, prevents over-segmentation of clusters, reduces noise points, effectively shields the algorithm\'s sensitivity to input parameters, improves the quality of clustering results, and reduces the impact of density gaps on clustering results. The high execution efficiency of the DBSCAN algorithm is maintained and applied to bus station clustering in the intelligent bus transfer query engine. The clustering accuracy rate is improved by 16%, which verifies the effectiveness of the new algorithm. Keywords: clustering; DBSCAN algorithm; parameter sensitivity; data mining

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