發(fā)布時間：2018-05-13 09:35

  本文選題：流聚類 + 仿射傳播��； 參考：《西安交通大學學報》2017年03期

【摘要】：針對目前流聚類算法無法有效處理數(shù)據(jù)流離群點的檢測和處理,以及增量式數(shù)據(jù)流聚類效率較低等問題,提出了一種基于密度度量的異常檢測、刪除的增強型仿射傳播流聚類算法。在仿射傳播流聚類算法的基礎上,所提算法通過引進異常檢測和刪除機制改善了異常點對聚類精度、聚類效率的影響。利用仿射傳播聚類實現(xiàn)在線數(shù)據(jù)流的聚類過程,同時檢測數(shù)據(jù)漂移現(xiàn)象,即數(shù)據(jù)流分布特征隨時間發(fā)生變化,并采用基于密度度量的局部異常因子檢測技術(LOF)對儲備池數(shù)據(jù)進行異常檢測和刪除處理,通過對當前類簇和處理過的儲備池數(shù)據(jù)重聚類來重建動態(tài)數(shù)據(jù)流模型。在真實網(wǎng)絡數(shù)據(jù)(KDD’99)上進行了實驗,結果表明,所提算法不僅減少了重聚類構建動態(tài)模型的次數(shù),改善了聚類效率,而且在同時考慮聚類精度、純度和熵3種聚類評價標準下,均優(yōu)于傳統(tǒng)的仿射傳播流聚類算法。
[Abstract]:Aiming at the problem that current flow clustering algorithm can not effectively deal with outlier detection and processing of data stream, and the efficiency of incremental data stream clustering is low, a density metric based anomaly detection method is proposed. Deletes an enhanced affine propagation flow clustering algorithm. Based on the affine propagation flow clustering algorithm, the proposed algorithm improves the effect of outlier points on clustering accuracy and clustering efficiency by introducing anomaly detection and deletion mechanisms. The affine propagation clustering is used to realize the online data flow clustering process, and the data drift phenomenon is detected at the same time, that is, the distribution characteristics of the data flow change with time. The local anomaly factor detection technique based on density metric is used to detect and delete the data of the reserve pool, and the dynamic data flow model is reconstructed by clustering the current cluster and the processed data of the storage pool. The experimental results on the real network data show that the proposed algorithm not only reduces the number of times of reclustering to construct dynamic model, but also improves the clustering efficiency, and considers the clustering accuracy at the same time. It is superior to the traditional affine propagation flow clustering algorithm under three clustering criteria of purity and entropy.
【作者單位】： 西安交通大學軟件學院;西安交通大學電子與信息工程學院;
【基金】：國家自然科學基金資助項目(61371087,61531013) 國家“863計劃”資助項目(2015AA015702)
【分類號】：TP311.13

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本文編號：1882612