一種基于CFSFDP改進算法的重要地點識別方法研究
發(fā)布時間：2018-12-09 19:01
【摘要】：為解決CFSFDP聚類算法由于無法自動選擇簇中心點而難以應用于重要地點識別的問題,引入一種簇中心點自動選擇策略對算法進行改進。該策略將簇中心點權值的變化趨勢作為自動劃分簇中心的依據(jù),有效避免了通過決策圖判決簇中心點的方法所帶來的誤差。將CFSFDP改進算法與數(shù)據(jù)預處理及逆向地理編碼等技術結合起來,能夠以較高的精度實現(xiàn)重要地點識別。實驗以Foursquare數(shù)據(jù)為例,結果表明CFSFDP改進算法比DBSCAN具有更高的準確率和較低的計算量,進一步證明了該方法在處理稀疏位置數(shù)據(jù)的重要地點識別問題上具有一定優(yōu)越性。
[Abstract]:In order to solve the problem that CFSFDP clustering algorithm is difficult to be applied to the identification of important points because of its inability to automatically select cluster center points, an automatic selection strategy for cluster center points is introduced to improve the algorithm. The strategy takes the change trend of weight value of cluster center as the basis of automatic classification of cluster center, and effectively avoids the error caused by the method of deciding cluster center point by decision graph. By combining the improved CFSFDP algorithm with data preprocessing and reverse geographic coding techniques, important location recognition can be realized with high accuracy. Taking Foursquare data as an example, the results show that the improved CFSFDP algorithm has higher accuracy and lower computational complexity than DBSCAN, and further proves that this method has some advantages in dealing with the important location identification problem of sparse position data.
【作者單位】： 電子工程學院;
【基金】：國防重點實驗室基金資助項目(9140C130104)
【分類號】：TP311.13
，
本文編號：2369879
[Abstract]:In order to solve the problem that CFSFDP clustering algorithm is difficult to be applied to the identification of important points because of its inability to automatically select cluster center points, an automatic selection strategy for cluster center points is introduced to improve the algorithm. The strategy takes the change trend of weight value of cluster center as the basis of automatic classification of cluster center, and effectively avoids the error caused by the method of deciding cluster center point by decision graph. By combining the improved CFSFDP algorithm with data preprocessing and reverse geographic coding techniques, important location recognition can be realized with high accuracy. Taking Foursquare data as an example, the results show that the improved CFSFDP algorithm has higher accuracy and lower computational complexity than DBSCAN, and further proves that this method has some advantages in dealing with the important location identification problem of sparse position data.
【作者單位】： 電子工程學院;
【基金】：國防重點實驗室基金資助項目(9140C130104)
【分類號】：TP311.13
，
本文編號：2369879
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