Online Web mining transactions association rules using frame metadata model

2002 ◽  
Author(s):  
J. Fong ◽  
J.G. Hughes ◽  
Jianhan Zhu
Keyword(s):  
Association Rules ◽  
Web Mining ◽  
Metadata Model ◽  
Online Web

An Efficient Genetic Simulated Annealing Association Rules Method

2010 ◽  
Vol 34-35 ◽  
pp. 927-931
Author(s):  
Jun Jie Cen ◽  
Guo Hong Gao ◽  
Ying Jun Wang
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Data Analysis ◽  
Association Rules ◽  
Association Rule ◽  
Web Site ◽  
Web Mining ◽  
Incremental Mining ◽  
Mining Technology

Association rule is one of the important models of Web mining. By analyzing the topology of web site, this paper brings forward an efficient genetic simulated annealing association rules method．It applies genetic algorithm，incremental mining technology to trace users access behavior and optimizes association rules，and forecast capable association rules which improves its precision．Finally, this paper gives out the data analysis of experiment and summarizes the characteristics of genetic mining．

Research and Application of Apriori Algorithm for Mining Association Rules

2014 ◽  
Vol 1079-1080 ◽  
pp. 737-742
Author(s):  
Yi Yong Ye
Keyword(s):  
Association Analysis ◽  
Association Rules ◽  
Web Mining ◽  
Recommendation System ◽  
Apriori Algorithm ◽  
Mining Algorithm ◽  
Common Technique ◽  
Mining Association Rules

For large amounts of data generated by the e-commerceplatform, combining with the actual needs of e-commerce recommendation system,make research on a common technique of association rules which orientede-commerce Web mining association analysis, introduces the association rules ofApriori mining algorithm, and the specific application of Apriori algorithm isanalyzed through a practical example, Finally, point out the shortcomings ofclassical Apriori algorithm, and gives directions for improvement.

Research on the Web Mining Algorithm Application in Tourism E-Commerce

2013 ◽  
Vol 380-384 ◽  
pp. 1133-1136
Author(s):  
Xue Song Zhao ◽  
Kai Fan Ji
Keyword(s):  
Association Rules ◽  
Clustering Analysis ◽  
Web Mining ◽  
Web Design ◽  
Developed Countries ◽  
Online Sales ◽  
Mining Algorithm ◽  
Personalized Services ◽  
Mining Algorithms ◽  
The Web

Web mining algorithms are widely used in e-commerce. Tourism e-commerce develops fast in recent years in China but the application of web mining algorithms stays in low level compared with some developed countries. This paper first discusses two major web mining algorithms: the Association Rules algorithm and Clustering Analysis, and then analyzes the application of web mining algorithm in tourism e-commerce. It concludes that web mining algorithms can help tourism e-commerce to improve web design, increase online sales and provide better personalized services for web users.

scholarly journals Web Personalization With Web Usage Mining Technics and Association Rules

2015 ◽  
Vol 15 (1) ◽  
pp. 6394-6401 ◽  
Author(s):  
G. Kazeminuri ◽  
A. Harounabadi ◽  
J. Mirabedini
Keyword(s):  
Association Rules ◽  
Web Mining ◽  
Information Overload ◽  
Relevant Information ◽  
Web Personalization ◽  
Efficient Manner ◽  
Leading Role ◽  
Hidden Knowledge ◽  
Access To Data ◽  
The Web

As amount of information and web development increase considerably, some technics and methods are required to allow efficient access to data and information extraction from them. Extracting useful pattern from worldwide networks that are referred to as web mining is considered as one of the main applications of data mining. The key challenges of web users are exploring websites for finding the relevant information by taking minimum time in an efficient manner. Discovering the hidden knowledge in the manner of interaction in the web is considered as one of the most important technics in web utilization mining. Information overload is one of the main problems in current web and for tackling this problem the web personalization systems are presented that adapts the content and services of a website with user's interests and browsing behavior. Today website personalization is turned into a popular event for web users and it plays a leading role in speed of access and providing users' desirable information. The objective of current article is extracting index based on users' behavior and web personalization using web mining technics based on utilization and association rules. In proposed methods the weighting criteria showing the extent of interest of users to the pages are expressed and a method is presented based on combination of association rules and clustering by perceptron neural network for web personalization. The proposed method simulation results suggest the improvement of precision and coverage criteria with respect to other compared methods.

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