基于方差优化谱聚类的热点区域挖掘算法

为改善交通拥堵的情况，本文利用聚类分析方法对移动轨迹数据进行挖掘，识别居民出行的热点区域。传统的Ng-Jordan-Weiss （NJW）谱聚类算法常使用K-means聚类算法来实现最后的聚类操作，然而K-means聚类算法存在对初始值敏感、容易陷入局部最优的缺陷，影响对热点区域的挖掘结果。因此，本研究将方差优化初始中心的K-medoids聚类算法运用到谱聚类算法最后聚类阶段，提出基于方差优化谱聚类的热点区域挖掘算法（Hot Region Mining algorithm based on improved K-medoids Spectral Clustering，HRM-KSC），然后在真实的轨迹数据集上进行试验。试验结果发现，HRM-KSC算法聚类结果的轮廓系数更高，表明HRM-KSC算法改善了NJW谱聚类算法，提高了聚类质量。

Hot Region Mining Algorithm based on Variance Optimization Spectrum Clustering

In order to improve the traffic congestion,this article uses the cluster analysis approach to mine the mobile trajectory data and identify the hot region of residents'' travel. The traditional Ng-Jordan-Weiss (NJW) spectral clustering algorithm often uses K-means clustering algorithm to achieve the final clustering operation. However,K-means clustering algorithm has the disadvantages of being sensitive to the initial value and easy to fall into the local optimum,which will affect the mining results of hotspot area. Therefore,the K-medoids clustering algorithm of variance optimization initial center is applied to the final clustering stage of the spectral clustering algorithm,and a Hot Region Mining algorithm based on improved K-medoids Spectral Clustering (HRM-KSC) is proposed,and then experiment on real trajectory data sets. The experiment results find that the HRM-KSC algorithm clustering results have higher silhouette coefficient,which indicates that the HRM-KSC algorithm improves the NJW spectral clustering algorithm and the clustering quality.