scholarly journals A Review on Clustering Techniques: Creating Better User Experience for Online Roadshow

2021 ◽  
Vol 13 (9) ◽  
pp. 233
Author(s):  
Zhou-Yi Lim ◽  
Lee-Yeng Ong ◽  
Meng-Chew Leow
Keyword(s):  
User Experience ◽  
Similarity Measures ◽  
Web Usage Mining ◽  
Advertising Effectiveness ◽  
Clustering Techniques ◽  
Digital Devices ◽  
Web Usage ◽  
User Access ◽  
Access Patterns ◽  
Functional Purpose

Online roadshow is a relatively new concept that has higher flexibility and scalability compared to the physical roadshow. This is because online roadshow is accessible through digital devices anywhere and anytime. In a physical roadshow, organizations can measure the effectiveness of the roadshow by interacting with the customers. However, organizations cannot monitor the effectiveness of the online roadshow by using the same method. A good user experience is important to increase the advertising effects on the online roadshow website. In web usage mining, clustering can discover user access patterns from the weblog. By applying a clustering technique, the online roadshow website can be further improved to provide a better user experience. This paper presents a review of clustering techniques used in web usage mining, namely the partition-based, hierarchical, density-based, and fuzzy clustering techniques. These clustering techniques are analyzed from three perspectives: their similarity measures, the evaluation metrics used to determine the optimality of the clusters, and the functional purpose of applying the techniques to improve the user experience of the website. By applying clustering techniques in different stages of the user activities in the online roadshow website, the advertising effectiveness of the website can be enhanced in terms of its affordance, flow, and interactivity.

The Development and Design of Intelligent Web Site Based on Web Usage Mining

2013 ◽  
Vol 718-720 ◽  
pp. 2074-2079
Author(s):  
Ting Zhong Wang ◽  
Gang Long Fan
Keyword(s):  
Web Site ◽  
Data Extraction ◽  
Web Server ◽  
Web Usage Mining ◽  
Business Decision ◽  
Web Usage ◽  
User Data ◽  
User Access ◽  
Access Patterns ◽  
The Web

Web usage mining is the information about the user data extraction, transformation, analysis and model processing, extracted from the auxiliary business decision of key data. Intelligent site refers to the use of the Web server log for user access patterns and provide personalized service for users. The paper proposes the development and design of intelligent web site based on web usage mining. This paper presents the access interest measure method and the traditional consider only clicks visit interest measure method, recommend less deviation, has better recommendation results.

Rule Optimization of Web-Logs Data Using Evolutionary Technique

2014 ◽  
pp. 180-192
Author(s):  
Manish Kumar ◽  
Sumit Kumar
Keyword(s):  
Web Server ◽  
Web Usage Mining ◽  
Web Pages ◽  
Web Usage ◽  
Data Application ◽  
Level Data ◽  
Typical Data ◽  
Evolutionary Technique ◽  
User Access ◽  
Access Patterns

Web usage mining can extract useful information from Weblogs to discover user access patterns of Web pages. Web usage mining itself can be classified further depending on the kind of usage data. This may consider Web server data, application server data, or application level data. Web server data corresponds to the user logs that are collected at Web servers. Some of the typical data collected at Web server are the URL requested, the IP address from which the request originated, and timestamp. Weblog data is required to be cleaned, condensed, and transformed in order to retrieve and analyze significant and useful information. This chapter analyzes access frequent patterns by applying the FP-growth algorithm, which is further optimized by using Genetic Algorithm (GA) and fuzzy logic.

Rule Optimization of Web-Logs Data Using Evolutionary Technique

2015 ◽  
pp. 158-169
Author(s):  
Manish Kumar ◽  
Sumit Kumar
Keyword(s):  
Web Server ◽  
Web Usage Mining ◽  
Web Pages ◽  
Web Usage ◽  
Data Application ◽  
Level Data ◽  
Typical Data ◽  
Evolutionary Technique ◽  
User Access ◽  
Access Patterns

Web usage mining can extract useful information from Weblogs to discover user access patterns of Web pages. Web usage mining itself can be classified further depending on the kind of usage data. This may consider Web server data, application server data, or application level data. Web server data corresponds to the user logs that are collected at Web servers. Some of the typical data collected at Web server are the URL requested, the IP address from which the request originated, and timestamp. Weblog data is required to be cleaned, condensed, and transformed in order to retrieve and analyze significant and useful information. This chapter analyzes access frequent patterns by applying the FP-growth algorithm, which is further optimized by using Genetic Algorithm (GA) and fuzzy logic.

scholarly journals Penggunaan Metode berbasis Graph untuk Mining Frequent Sequential Access Pattern Pada Studi Kasus : Website iGracias Universitas Telkom

2017 ◽  
Vol 2 (1) ◽  
pp. 91 ◽  
Author(s):  
Rahmi Rohdiniyah ◽  
Ibnu Asror ◽  
Gede Agung Ary Wisudawan
Keyword(s):  
Web Mining ◽  
Web Usage Mining ◽  
Access Pattern ◽  
Web Usage ◽  
Access Patterns

Penggunaan <em>website</em> pada bidang pendidikan, khususnya sebuah universitas, bertujuan untuk menyimpan berbagai informasi yang ada pada lingkungan universitas tersebut. Untuk itu, perlu dilakukan perbaikan struktur untuk memelihara kualitas dari web. Salah satu teknik yang dapat digunakan adalah dengan menggunakan <em>web usage mining. Web usage mining</em> merupakan salah satu cabang dari <em>web mining</em> yang digunakan untuk menemukan informasi atau pengetahuan yang bermanfaat dari pola navigasi <em>user </em>pada sebuah<em> website</em>. Pada penelitian ini menggunakan metode berbasis graph untuk <em>frequent sequential access patterns</em> dan menggunakan Igracias Universitas Telkom sebagai studi kasusnya. Karena Igracias selalu digunakan oleh seluruh entitas yang ada pada Universitas Telkom. Metode ini  memiliki kelebihan untuk menemukan <em>behavior</em> pola pengaksesan <em>user</em>. Dari implementasi metoda ini didapat pola akses group user secara berurutan.

scholarly journals A review of alignment based similarity measures for web usage mining

2019 ◽  
Vol 53 (3) ◽  
pp. 1529-1551
Author(s):  
Vinh-Trung Luu ◽  
Germain Forestier ◽  
Jonathan Weber ◽  
Paul Bourgeois ◽  
Fahima Djelil ◽  
...  
Keyword(s):  
Similarity Measures ◽  
Web Usage Mining ◽  
Web Usage

scholarly journals Optimal Biclustering using Hybrid Swarm Intelligence for Web usage Mining

2019 ◽  
Vol 8 (2) ◽  
pp. 6392-6395
Keyword(s):  
Swarm Intelligence ◽  
Web Usage Mining ◽  
Web Pages ◽  
Crossover Operator ◽  
Critical Research ◽  
Hybrid Swarm ◽  
Swarm Optimization ◽  
Clustering Techniques ◽  
Web Usage ◽  
Browsing Behavior

Web usage mining is used to analyze the user browsing behavior among the web pages which can be further utilized in other applications like recommender system, personalized web pages, providing insight for better business functionality. Since this type of mining does not only depends on the user or web pages, conventional clustering techniques may not suit very well for the analysis. Biclustering techniques are used to discover the subset in the form of submatrices as objects and attributes of objects are considered symmetrically. Finding optimal biclusters is a critical research issue. This research proposes a hybrid swarm intelligence-based method having Particle Swarm Optimization combined with Leader Clustering method along with Uniform Crossover operator. The experimental study shows that the proposed method performs well than traditional biclustering techniques in terms of evaluation metrics.

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