CAJ | 학술논문

基于Web日志的信息挖掘具有重要的意义,比如识别兴趣相似的客户群体有利于实现推荐和个性化服务.采用了多元线性回归分析用户浏览行为,直接对兴趣相似矩阵进行入截聚类,最后通过计算项与类的连接强度来调整聚类结果.实验结果证明了该算法具有较高的准确率和良好的扩展性.
기우Web일지적신식알굴구유중요적의의,비여식별흥취상사적객호군체유리우실현추천화개성화복무.채용료다원선성회귀분석용호류람행위,직접대흥취상사구진진행입절취류,최후통과계산항여류적련접강도래조정취류결과.실험결과증명료해산법구유교고적준학솔화량호적확전성.
Data mining based on Web logs is of great significance.For instance,it can discover groups of people with similar interests and facilitate recommendation and personal service.A new clustering method based on Web users' interests regressively analyzes users' behaviors,partitions the interesting matrix with a threshold λ,and finally relocates some elements of clusters based on the joint strength between an element and a cluster.The favorable precision and scalability of the algorithm are studied through the experiments.