为研究地铁站的精细化分类问题,利用基于出行链分析的通勤出行识别方法筛选通勤客流,结合早晚高峰的进出站客流量,识别车站的职住功能特征;基于百度地图开源平台抓取POI(Point of interest)数据,从用地功能角度进行组合归类,得到细粒度...为研究地铁站的精细化分类问题,利用基于出行链分析的通勤出行识别方法筛选通勤客流,结合早晚高峰的进出站客流量,识别车站的职住功能特征;基于百度地图开源平台抓取POI(Point of interest)数据,从用地功能角度进行组合归类,得到细粒度的车站周边土地利用特征。结合以上两类特征,建立基于非监督学习K-Means++方法的地铁车站分类模型,将北京地铁307个车站分为7类。根据其客流和周边用地特征分别识别为配套设施开发完善的典型居住型车站,具有商业开发潜力的典型居住型车站,配置一定工作岗位的居住型车站,高度开发的典型工作型车站,职住结合的工作型车站,旅游休闲型的车站,以及尚待开发的远郊车站。经过分析,该分类结果与实际情况高度吻合,验证了模型的有效性,可以为城市规划及车站周边土地开发提供依据。展开更多
Though K-means is very popular for general clustering, its performance, which generally converges to numerous local minima, depends highly on initial cluster centers. In this paper a novel initialization scheme to sel...Though K-means is very popular for general clustering, its performance, which generally converges to numerous local minima, depends highly on initial cluster centers. In this paper a novel initialization scheme to select initial cluster centers for K-means clustering is proposed. This algorithm is based on reverse nearest neighbor （RNN） search which retrieves all points in a given data set whose nearest neighbor is a given query point. The initial cluster centers computed using this methodology are found to be very close to the desired cluster centers for iterative clustering algorithms. This procedure is applicable to clustering algorithms for continuous data. The application of the proposed algorithm to K-means clustering algorithm is demonstrated. An experiment is carried out on several popular datasets and the results show the advantages of the proposed method.展开更多
文摘为研究地铁站的精细化分类问题,利用基于出行链分析的通勤出行识别方法筛选通勤客流,结合早晚高峰的进出站客流量,识别车站的职住功能特征;基于百度地图开源平台抓取POI(Point of interest)数据,从用地功能角度进行组合归类,得到细粒度的车站周边土地利用特征。结合以上两类特征,建立基于非监督学习K-Means++方法的地铁车站分类模型,将北京地铁307个车站分为7类。根据其客流和周边用地特征分别识别为配套设施开发完善的典型居住型车站,具有商业开发潜力的典型居住型车站,配置一定工作岗位的居住型车站,高度开发的典型工作型车站,职住结合的工作型车站,旅游休闲型的车站,以及尚待开发的远郊车站。经过分析,该分类结果与实际情况高度吻合,验证了模型的有效性,可以为城市规划及车站周边土地开发提供依据。
基金Supported by the National Natural Science Foundation of China (60503020, 60503033, 60703086)the Natural Science Foundation of Jiangsu Province (BK2006094)+1 种基金the Opening Foundation of Jiangsu Key Labo-ratory of Computer Information Processing Technology in Soochow University ( KJS0714)the Research Foundation of Nanjing University of Posts and Telecommunications (NY207052, NY207082)
文摘Though K-means is very popular for general clustering, its performance, which generally converges to numerous local minima, depends highly on initial cluster centers. In this paper a novel initialization scheme to select initial cluster centers for K-means clustering is proposed. This algorithm is based on reverse nearest neighbor （RNN） search which retrieves all points in a given data set whose nearest neighbor is a given query point. The initial cluster centers computed using this methodology are found to be very close to the desired cluster centers for iterative clustering algorithms. This procedure is applicable to clustering algorithms for continuous data. The application of the proposed algorithm to K-means clustering algorithm is demonstrated. An experiment is carried out on several popular datasets and the results show the advantages of the proposed method.
