CAJ | 학술논문

针对电子商务系统中大多采取用户评分或购买数据进行聚类，较少进一步分析用户行为的现状，提出一种根据用户浏览商品时序分析用户兴趣的方法。在此基础上先用 CanoPy 算法进行数据预处理后使用 K ﹣均值算法根据用户兴趣实现用户聚类。采用 KDD CUP2000数据集中的用户点击流数据中的用户浏览记录对算法进行实验，实验结果表明算法有较好的聚类结果。
침대전자상무계통중대다채취용호평분혹구매수거진행취류，교소진일보분석용호행위적현상，제출일충근거용호류람상품시서분석용호흥취적방법。재차기출상선용 CanoPy 산법진행수거예처리후사용 K ﹣균치산법근거용호흥취실현용호취류。채용 KDD CUP2000수거집중적용호점격류수거중적용호류람기록대산법진행실험，실험결과표명산법유교호적취류결과。
This PaPer Presents a method based on analyzingthe users' interesting while their browsing goods, aiming atfurther exPloring the current situation of their behaviors,as the e ﹣ commerce conducts the clustering based on the user rating data or user Purchase data,less doesthe further analysis of their behaviors. On this basis, the CanoPy algorithm was used to PreProcess the data,and then use K ﹣ means algorithm to do the user cluste-ring. And in the end,the user click stream data of Gazelle. com Provided by the KDD CuP 2000 was used to vali-date the algorithm,the exPerimental results shows that the algorithm has better clustering results.