本文選題：K-means算法 + 傳統(tǒng)R樹��； 參考：《小型微型計算機系統(tǒng)》2016年02期

【摘要】：針對K-means方法的不足,提出CUK-MEANS算法,用以解決K-MEANS方法在初始值選擇上的不足和對噪聲點敏感的問題.傳統(tǒng)R樹索引是動態(tài)生成的,通過節(jié)點的連續(xù)插入和分裂實現(xiàn)整個索引的構(gòu)建,這種方法會造成大量的外包矩形重疊,從而導致索引效率不高.基于CUK-MEANS算法本文進一步提出了CKR-R()算法,利用聚類技術(shù)對數(shù)據(jù)進行預(yù)處理,減少節(jié)點之間的重疊度,提高了R樹的索引效率,并且采用收縮因子使節(jié)點內(nèi)數(shù)據(jù)更加緊湊,提高節(jié)點的空間利用率.理論研究和實驗表明所提算法具有較高的查詢效率.
[Abstract]:Aiming at the shortage of K-means method, a CUK-MEANS algorithm is proposed to solve the problem of K-MEANS method in selecting initial value and being sensitive to noise points. The traditional R-tree index is dynamically generated. The whole index is constructed by the continuous insertion and splitting of nodes. This method will result in a large number of outsourced rectangular overlaps resulting in low index efficiency. Based on CUK-MEANS algorithm, this paper proposes CKR-RN) algorithm, which uses clustering technology to preprocess the data, reduces the overlap between nodes, improves the index efficiency of R-tree, and uses contraction factor to make the data of nodes more compact. Improve the space utilization of nodes. Theoretical research and experiments show that the proposed algorithm has a high query efficiency.
【作者單位】： 哈爾濱理工大學計算機科學與技術(shù)學院;
【基金】：國家自然科學基金項目(61370084)資助 黑龍江省自然科學基金項目(F201302)資助 黑龍江省教育廳科學研究項目(12541128;12531z004)資助
【分類號】：P208;TP311.13
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本文編號：1858517