Web Fuzzy Clustering Processing Model

2011 ◽  
Vol 219-220 ◽  
pp. 98-102
Author(s):  
Kai Xi Xie ◽  
Ting Gui Chen
Keyword(s):  
Fuzzy Clustering ◽  
Web Mining ◽  
Web Usage Mining ◽  
Web Page ◽  
Clustering Method ◽  
Web Usage ◽  
User Clustering ◽  
Web Page Clustering ◽  
Web User Clustering ◽  
The Web

In this paper, we combine the web mining and fuzzy clustering and give the concept of web fuzzy clustering processing model and its application. We also introduce the web fuzzy direct clustering method in brief. Web fuzzy clustering can be used in the web user clustering and web page clustering of web usage mining.

Website Structure Improvement Based on the Combination of Selected Web Structure and Web Usage Mining Methods

2018 ◽  
Vol 17 (06) ◽  
pp. 1743-1776 ◽  
Author(s):  
Jozef Kapusta ◽  
Michal Munk ◽  
Martin Drlik
Keyword(s):  
Web Mining ◽  
Web Usage Mining ◽  
Point Of View ◽  
Web Pages ◽  
Web Page ◽  
Web Usage ◽  
Web Structure ◽  
Web Structure Mining ◽  
Mining Methods ◽  
The Web

The different web mining methods and techniques can help to solve some typical issues of the contemporary websites, contribute to more effective personalization, improve a website structure and reorganize its web pages. However, only several papers tried to combine web structure and web usage mining (WUM) methods with this aim. The paper researches if and how the combination of selected web structure and WUM methods can identify misplaced web pages and how they can contribute to improving the website structure. The paper analyzes the relationship between the estimated importance of the web page from the web page creator’s point of view using the web structure mining method based on PageRank and visitors’ real perception of the importance of that individual web page using the WUM method based on sequence patterns analysis, which eliminates the problem with repeated visits of the same web page during one session. The results prove that the expected probability of accesses to the individual web page correlates with the observed visit rate obtained from the log files using the WUM method. Furthermore, the website can be improved based on the consequent application of the residual analysis on the obtained results. The applicability of the proposed combination of the web structure and WUM methods is presented on two case studies from different application domains of the contemporary web. As a result, the web pages, which are underestimated or overestimated by the web page creators, are successfully identified in both cases.

Web Usage Mining through Associative Models

2011 ◽  
pp. 1231-1234 ◽  
Author(s):  
Paolo Giudici ◽  
Paola Cerchiello
Keyword(s):  
Web Site ◽  
Web Usage Mining ◽  
Web Pages ◽  
Web Page ◽  
User Session ◽  
Web Usage ◽  
Associative Models ◽  
The Web

The aim of this contribution is to show how the information, concerning the order in which the pages of a Web site are visited, can be profitably used to predict the visit behaviour at the site. Usually every click corresponds to the visualization of a Web page. Thus, a Web clickstream defines the sequence of the Web pages requested by a user. Such a sequence identifies a user session.

Web Mining by Automatically Organizing Web Pages into Categories

2011 ◽  
pp. 214-231 ◽  
Author(s):  
Ben Choi
Keyword(s):  
Artificial Intelligence ◽  
Web Mining ◽  
Clustering Algorithm ◽  
Constant Factor ◽  
Web Pages ◽  
Web Page ◽  
Number Of Clusters ◽  
Web Contents ◽  
Web Page Clustering ◽  
The Web

Web mining aims for searching, organizing, and extracting information on the Web and search engines focus on searching. The next stage of Web mining is the organization of Web contents, which will then facilitate the extraction of useful information from the Web. This chapter will focus on organizing Web contents. Since a majority of Web contents are stored in the form of Web pages, this chapter will focus on techniques for automatically organizing Web pages into categories. Various artificial intelligence techniques have been used; however the most successful ones are classification and clustering. This chapter will focus on clustering. Clustering is well suited for Web mining by automatically organizing Web pages into categories each of which contain Web pages having similar contents. However, one problem in clustering is the lack of general methods to automatically determine the number of categories or clusters. For the Web domain, until now there is no such a method suitable for Web page clustering. To address this problem, this chapter describes a method to discover a constant factor that characterizes the Web domain and proposes a new method for automatically determining the number of clusters in Web page datasets. This chapter also proposes a new bi-directional hierarchical clustering algorithm, which arranges individual Web pages into clusters and then arranges the clusters into larger clusters and so on until the average inter-cluster similarity approaches the constant factor. Having the constant factor together with the algorithm, this chapter provides a new clustering system suitable for mining the Web.

Statistical Methods for User Profiling in Web Usage Mining

2010 ◽  
pp. 359-368 ◽  
Author(s):  
Marcello Pecoraro
Keyword(s):  
Statistical Methods ◽  
Web Mining ◽  
Web Usage Mining ◽  
User Profiling ◽  
Web Usage ◽  
Data Object ◽  
Segmentation Methods ◽  
Binary Segmentation ◽  
Usage Analysis ◽  
The Web

This chapter aims at providing an overview about the use of statistical methods supporting the Web Usage Mining. Within the first part is described the framework of the Web Usage Mining as a branch of the Web Mining committed to the study of how to use a Website. Then, the data (object of the analysis) are detailed together with the problems linked to the pre-processing. Once clarified, the data origin and their treatment for a correct development of a Web Usage analysis,the focus shifts on the statistical techniques that can be applied to the analysis background, with reference to binary segmentation methods. Those latter allow the discrimination through a response variable that determines the affiliation of the users to a group by considering some characteristics detected on the same users.

Performance Comparison of Data Mining Classifiers on Web Log Data

2020 ◽  
Vol 17 (11) ◽  
pp. 5113-5116
Author(s):  
Varun Malik ◽  
Vikas Rattan ◽  
Jaiteg Singh ◽  
Ruchi Mittal ◽  
Urvashi Tandon
Keyword(s):  
Web Mining ◽  
Performance Comparison ◽  
Web Usage Mining ◽  
Classification Algorithms ◽  
Web Content ◽  
Web Usage ◽  
Web Structure ◽  
Web Structure Mining ◽  
Content Mining ◽  
The Web

Web usage mining is the branch of web mining that deals with mining of data over the web. Web mining can be categorized as web content mining, web structure mining, web usage mining. In this paper, we have summarized the web usage mining results executed over the user tool WMOT (web mining optimized tool) based on the WEKA tool that has been used to apply various classification algorithms such as Naïve Bayes, KNN, SVM and tree based algorithms. Authors summarized the results of classification algorithms on WMOT tool and compared the results on the basis of classified instances and identify the algorithms that gives better instances accuracy.

scholarly journals A Review on Design and Development Of Sequential Patterns Algorithms In Web Usage Mining

2021 ◽  
Vol 12 (2) ◽  
pp. 1634-1639
Author(s):  
V Aruna, Et. al.
Keyword(s):  
Web Mining ◽  
Pattern Mining ◽  
Pattern Discovery ◽  
Relevant Information ◽  
Sequential Pattern Mining ◽  
Web Usage Mining ◽  
Sequential Pattern ◽  
Web Usage ◽  
Hidden Data ◽  
The Web

In the recent years with the advancement in technology, a  lot of information is available in different formats and extracting the  knowledge from that data has become a very difficult task. Due to the vast amount of information available on the web, users are finding it difficult to extract relevant information or create new knowledge using information available on the web. To solve this problem  Web mining techniques are used to discover the interesting patterns from the hidden data .Web Usage Mining (WUM), which is one  of the subset of  Web Mining helps in extracting the hidden knowledge present in the Web log  files , in recognizing various interests of web users and also in  discovering customer behaviours. Web Usage mining  includes different phases of data mining techniques called Data Pre-processing, Pattern Discovery & Pattern Analysis. This paper presents an updated focused survey on various sequential pattern mining  algorithms  like  apriori-based algorithm , Breadth First Search-based strategy, Depth First Search strategy,  sequential closed-pattern algorithm and Incremental pattern mining algorithm which are used in Pattern Discovery Phase of WUM. At last , a comparison  is done based on the important key features present in these algorithms. This study gives us better understanding of the approaches of sequential pattern mining.

scholarly journals Extraction of Knowledge from Web Server Logs Using Web Usage Mining

2019 ◽  
Vol 8 (S3) ◽  
pp. 12-15
Author(s):  
B. Harika ◽  
T. Sudha
Keyword(s):  
Data Mining ◽  
Web Mining ◽  
Web Server ◽  
Primary Source ◽  
Web Usage Mining ◽  
Data Mining Techniques ◽  
Web Usage ◽  
Browsing Behavior ◽  
The Web ◽  
Usage Data

Information on internet increases rapidly from day to day and the usage of the web also increases, thus there is the need to discover interesting patterns from web. The process used to extract and mine useful information from web documents by using Data Mining Techniques is called Web Mining. Web Mining is broadly classified in to three types namely Web Content Mining, Web Structure Mining and Web Usage Mining. In this paper our focus is mainly on Web Usage Mining, where we are applying the data mining techniques to analyse and discover interesting knowledge from the Web Usage data. The activities of the user are captured and stored at different levels such as server level, proxy level and user level called as Web Usage Data and the usage data stored at server side is Web Server Log, where it records the browsing behavior of users and their requests based on the user clicks. Web server Log is a primary source to perform Web Usage Mining. This paper also brings in to discussion of various existing pre-processing techniques and analysis of web log files and how clustering is applied to group the users based on the browsing behavior of users on their interested contents.

Taxonomical Classification of Web Usage Mining Applications and its Ontological Representation

2018 ◽  
Vol 7 (3) ◽  
pp. 39-43
Author(s):  
Satyaveer Singh ◽  
Mahendra Singh Aswal
Keyword(s):  
Web Usage Mining ◽  
Web Usage ◽  
Navigation Patterns ◽  
Product Recommendations ◽  
Taxonomical Classification ◽  
The Comparative Study ◽  
The Web ◽  
Real World Problems

Web usage mining is used to find out fascinating consumer navigation patterns which can be applied to a lot of real-world problems, such as enriching websites or pages, generating newly topic or product recommendations and consumer behavior studies, etc. In this paper, an attempt has been made to provide a taxonomical classification of web usage mining applications with two levels of hierarchy. Further, the ontology for various categories of the web usage mining applications has been developed and to prove the completeness of proposed taxonomy, a rigorous case study has been performed. The comparative study with other existing classifications of web usage mining applications has also been performed.

scholarly journals A Web Page Clustering Method Based on Formal Concept Analysis

Information ◽  
2018 ◽  
Vol 9 (9) ◽  
pp. 228 ◽  
Author(s):  
Zuping Zhang ◽  
Jing Zhao ◽  
Xiping Yan
Keyword(s):  
Formal Concept Analysis ◽  
Concept Lattice ◽  
Concept Analysis ◽  
Formal Context ◽  
Formal Concept ◽  
Web Pages ◽  
Web Page ◽  
Data Links ◽  
Web Page Clustering ◽  
The Web

Web page clustering is an important technology for sorting network resources. By extraction and clustering based on the similarity of the Web page, a large amount of information on a Web page can be organized effectively. In this paper, after describing the extraction of Web feature words, calculation methods for the weighting of feature words are studied deeply. Taking Web pages as objects and Web feature words as attributes, a formal context is constructed for using formal concept analysis. An algorithm for constructing a concept lattice based on cross data links was proposed and was successfully applied. This method can be used to cluster the Web pages using the concept lattice hierarchy. Experimental results indicate that the proposed algorithm is better than previous competitors with regard to time consumption and the clustering effect.

Sign in / Sign up

Export Citation Format

Share Document