Open AccessResearch on Clustering Algorithm Based on Web Log Mining
Author(s) -
Haifei Xiang
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1607/1/012102
Subject(s) - cluster analysis , computer science , web mining , data mining , the internet , process (computing) , data stream clustering , process mining , web service , class (philosophy) , cure data clustering algorithm , fuzzy clustering , world wide web , business process , machine learning , artificial intelligence , business process management , engineering , work in process , operations management , operating system
People’s online business behaviour has become more and more frequent. Internet service providers are beginning to find ways to obtain the interests and hobbies of users in order to provide targeted services to users. Analysis of user behavior based on Web logs can obtain valuable information from users. User clustering based on Web logs can cluster users according to user behavior, and then analyze user access patterns, providing a good solution for problem solving. This article introduces the concept and process of data mining, the classification and process of Web data mining, and then analyzes the K-Means clustering algorithm. The class-centered algorithm avoids clustering and only obtains the local optimal solution, and it can reduce the algorithm iteration time and improve the clustering quality.