计算机应用与软件
計算機應用與軟件
계산궤응용여연건
COMPUTER APPLICATIONS AND SOFTWARE
2014年
2期
283-286
,共4页
Web挖掘%Web用户聚类%聚类算法%模式聚类%k-means
Web挖掘%Web用戶聚類%聚類算法%模式聚類%k-means
Web알굴%Web용호취류%취류산법%모식취류%k-means
Web mining%Web users’clustering%Clustering algorithm%Pattern clustering k-means
Web用户聚类是通过分析用户会话，将具有相同或相似访问特征的用户聚为一类。在会话相似性度量方面综合考虑了网页浏览时间和访问频次两个因素，并考虑到用户个人习惯、能力等因素对浏览时间的影响，将浏览时间处理为RDP（Reduce the Differences in Personality）浏览时间，以降低其个性特征。为此，提出一种基于用户特性的RDPk-means聚类算法。实验表明，该算法可以有效实现用户会话的聚类，聚类结果客观合理。
Web用戶聚類是通過分析用戶會話，將具有相同或相似訪問特徵的用戶聚為一類。在會話相似性度量方麵綜閤攷慮瞭網頁瀏覽時間和訪問頻次兩箇因素，併攷慮到用戶箇人習慣、能力等因素對瀏覽時間的影響，將瀏覽時間處理為RDP（Reduce the Differences in Personality）瀏覽時間，以降低其箇性特徵。為此，提齣一種基于用戶特性的RDPk-means聚類算法。實驗錶明，該算法可以有效實現用戶會話的聚類，聚類結果客觀閤理。
Web용호취류시통과분석용호회화，장구유상동혹상사방문특정적용호취위일류。재회화상사성도량방면종합고필료망혈류람시간화방문빈차량개인소，병고필도용호개인습관、능력등인소대류람시간적영향，장류람시간처리위RDP（Reduce the Differences in Personality）류람시간，이강저기개성특정。위차，제출일충기우용호특성적RDPk-means취류산법。실험표명，해산법가이유효실현용호회화적취류，취류결과객관합리。
The Web users’clustering is to group the users with same or similar surfing behaviour into one class by analysing their ses-sions.In this paper,two factors of browsing time on webpage and visiting frequency are synthetically considered in sessions’similarity met-ric.In addition,the influence of other factors such as personal habit and ability on browsing time has also been taken into account.Browsing time is processed as RDP browsing time so as to reduce its personality characteristics.Therefore,we propose a personality characteristics-based RDPk-means clustering algorithm.Experiments show that this algorithm is effective in realising users’sessions clustering,the cluste-ring results are objective and reasonable.
