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Web Mining: A Survey of Current Research, Techniques, and Software

2008, International Journal of Information Technology & Decision Making

The purpose of this paper is to provide a more current evaluation and update of web mining research and techniques available. Current advances in each of the three different types of web mining are reviewed in the categories of web content mining, web usage mining, and web structure mining. For each tabulated research work, we examine such key issues as web mining process, methods/techniques, applications, data sources, and software used. Unlike previous investigators, we divide web mining processes into the following five subtasks: (1) resource finding and retrieving, (2) information selection and preprocessing, (3) patterns analysis and recognition, (4) validation and interpretation, and (5) visualization. This paper also reports the comparisons and summaries of selected software for web mining. The web mining software selected for discussion and comparison in this paper are SPSS Clementine, Megaputer PolyAnalyst, ClickTracks by web analytics, and QL2 by QL2 Software Inc. Applicat...

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anurag kumar

Web Mining is moving the World Wide Web towards a more useful environment in which users can quickly and easily find the information they need. Large amount of text documents, multimedia files and images are available in the web and it is still increasing. Data mining is the form of extracting data’s available in the internet. Web mining is a part of data mining. Web mining is used to discover and extract information from Web-related data sources such as Web documents, Web content, hyperlinks and server logs. The term Web mining has been used in three distinct ways. The first, called Web content mining is the process of information discovery from sources across the World Wide Web. The second, called Web structure mining is the process of analyzing the relationship between Web pages linked by information or direct link connection through the use of graph theory. The third, called Web usage mining is the process of extracting patterns and information from server logs to gain insight on user activity. In this paper, we are trying to give a brief idea regarding web mining concerned with its techniques, tools and applications.

Richard Segall

Venkata Ramana

brijesh singh

Bonfring International Journal

Web is a platforms for information exchange, as it is simple and easy to publish documents. Searching for information becomes a difficult and time-consuming process as the web grows. Web mining uses various data mining techniques to discover useful knowledge from usage log file from the web. The mining tools are used to scan the HTML documents, images, and text, the results is provided for the search engines.It can assist search engines in providing productive results of each search in order of their relevance. In this paper, we brief introduction to the concepts related to web mining and then an overview of different Web usage mining.

Dr. M.A.Dorairangaswamy

This study presents the role of Web mining an explosive growth of the World Wide Web; websites are providing an information and knowledge to the end users. This is the review paper which show deep and intense study of various technologies available for web mining and it is the application of data mining techniques to extract knowledge from web. Current advances in each of the three different types of web mining are reviewed in the categories of web content mining, web usage mining, and web structure mining. Index Terms—web mining, web content mining, web usage mining, web structure mining.

aarti Pandey

Jawad Mughal

Research Publish Journals

Abstract: Web mining is a very hot research topic which combines two of the activated research areas: Data Mining and World Wide Web. The Web mining research relates to several research communities such as Database, Information Retrieval and Artificial Intelligence. Although there exists quite some confusion about the Web mining, the most recognized approach is to categorize Web mining into three areas: Web content mining, Web structure mining, and Web usage mining. Web content mining focuses on the discovery/retrieval of the useful information from the Web contents/data/documents, while the Web structure mining emphasizes to the discovery of how to model the underlying link structures of the Web. The distinction between these two categories isn't a very clear sometimes. Web usage mining is relative independent, but not isolated, category, which mainly describes the techniques that discover the user's usage pattern and try to predict the user's behaviors. This paper is a survey based on the recently published research papers. Besides providing an overall view of Web mining, this paper will focus on Web usage mining. Generally speaking, Web usage mining consists of three phases: Pre-processing, Pattern discovery and Pattern analysis. A detailed description will be given for each part of them, however, special attention will be paid to the user navigation patterns discovery and analysis. The user privacy is another important issue in this paper. An example of a prototypical Web usage mining system, WebSIFT, will be introduced to make it easier to understand the methodology of how to apply data mining techniques to large Web data repositories in order to extract usage patterns. Finally, along with some other interested research issues, a brief overview of the current research work in the area of Web usage mining is included. Title: WEB MINING AN APPLICATION OF DATA MINING Author: Sumit Dalal, Sumit Kumar, Vivek Dixit International Journal of Computer Science and Information Technology Research ISSN 2348-120X (online), ISSN 2348-1196 (print) Research Publish Journals

maithreyan surya

The World Wide Web is a popular and interactive medium to disseminate information today. It is a system of interlinked hypertext documents accessed via the Internet. With a web browser, one can view web pages that may contain text, images, videos, and other multimedia, and navigate between them via hyperlinks. With the recent explosive growth of the amount of content on the Internet, it has become increasingly difficult for users to find and utilize information and for content providers to classify and catalog documents on the World Wide Web. Traditional web search engines often return hundreds or thousands of results for a search, which is time consuming for users to browse. On-line libraries, search engines, and other large document repositories (e.g. customer support databases, product specification databases, press release archives, news story archives, etc.) are growing so rapidly that it is difficult and costly to categorize every document manually. To deal with these problems web mining is used. Web mining is the use of data mining techniques to automatically discover and extract information from the web documents and services. This paper presents an overview of web mining, its methodologies, algorithms and applications.

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Text and Web Content Mining: A Systematic Review

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• First Online: 08 August 2021
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• Fatima Almatrooshi   ORCID: orcid.org/0000-0001-9446-273X 13 ,
• Sumayya Alhammadi   ORCID: orcid.org/0000-0002-0586-1403 13 ,
• Said A. Salloum   ORCID: orcid.org/0000-0002-6073-3981 14 , 15 &
• Khaled Shaalan   ORCID: orcid.org/0000-0003-0823-8390 13  

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 299))

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• International Conference on Emerging Technologies and Intelligent Systems

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Text and content mining are the subcategory of data mining. This category of data mining is used to extract the information from web or web pages of a website. This mining identifies useful information from web content like web pages, search logs, and other website-related content. The extracted information can be used in many applications, for example, we can extract opinions from online sources and web hierarchy which provides better insights and knowledge. In this paper, we conducted a systematic review that included 18 research papers that are relevant to the topic and matches the inclusion criteria of this study. From these research papers we were able to answer the research questions that we identified. The questions are related to the applications, techniques and issues of the text and web mining. The findings suggest that many research papers made a good foundation for this topic, and gave an informative explanation of each type of techniques used in text and web mining, as well as some issues that can be a future work for researchers who are interested in the topic.

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A Review: Web Content Mining Techniques

In Search of Insight from Unstructured Text Data: Towards an Identification of Text Mining Techniques

Web Structure Mining Algorithms: A Survey

Özyirmidokuz, E.K., Özyirmidokuz, M.H.: Analyzing customer complaints: a web text mining application. In: Proceedings of INTCESS14-International Conference on Education and Social Sciences, Istanbul, pp. 734–743 (2014)

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Hamid Mughal, M.J.: Data mining: web data mining techniques, tools and algorithms: an overview. Int. J. Adv. Comput. Sci. Appl. 9 (6), 208–215 (2018)

Milic, P.: Framework for Web Content Mining (2017)

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Acknowledgment

This work is a part of a project submitted in fulfilment of PhD Computer Science - Faculty of Engineering & Information Technology at The British University in Dubai.

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Fatima Almatrooshi, Sumayya Alhammadi & Khaled Shaalan

School of Science, Engineering, and Environment, University of Salford, Salford, UK

Said A. Salloum

Machine Learning and NLP Research Group, Department of Computer Science, University of Sharjah, Sharjah, UAE

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Almatrooshi, F., Alhammadi, S., Salloum, S.A., Shaalan, K. (2022). Text and Web Content Mining: A Systematic Review. In: Al-Emran, M., Al-Sharafi, M.A., Al-Kabi, M.N., Shaalan, K. (eds) Proceedings of International Conference on Emerging Technologies and Intelligent Systems. ICETIS 2021. Lecture Notes in Networks and Systems, vol 299. Springer, Cham. https://doi.org/10.1007/978-3-030-82616-1_8

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DOI: 10.14569/IJACSA.2021.0120886 PDF

A Systematic Review Web Content Mining Tools and its Applications

Author 1: Manjunath Pujar Author 2: Monica R Mundada

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 12 Issue 8, 2021.

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Abstract: In recent years, the emergence of WWW (World Wide Web) led to the accumulation of huge amount of information and data. Hence the web is found to consist of unstructured and structured information that impacts the day to day life of the society. Because of such availability of huge information, utilization of the required information becomes more challenging. This paper provided a comprehensive survey on the current situation and recent trends on web content mining (WCM) and its applications thereby contributing to the enhancement of the upcoming research in WCM. The paper focused mainly on the mining and retrieval techniques, various WCM approaches, challenges and process of information retrieval and information extraction. The paper describes the four major tasks of web content mining that is information retrieval, information extraction, generalization and validation in detail. WCM concentrates on orchestrating, sorting, classifying, collecting, congregating of web data and provide the improved data which can be easily accessed by the users. Web content mining tools were needed to scan text, images and HTML documents and provide results to the search engine. It guides the search engine to provide better productive results for every search based on their importance. The paper also analysed different web content mining tools for the extraction of relevant information from the corresponding web page.

Manjunath Pujar and Monica R Mundada, “A Systematic Review Web Content Mining Tools and its Applications” International Journal of Advanced Computer Science and Applications(IJACSA), 12(8), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0120886

@article{Pujar2021, title = {A Systematic Review Web Content Mining Tools and its Applications}, journal = {International Journal of Advanced Computer Science and Applications}, doi = {10.14569/IJACSA.2021.0120886}, url = {http://dx.doi.org/10.14569/IJACSA.2021.0120886}, year = {2021}, publisher = {The Science and Information Organization}, volume = {12}, number = {8}, author = {Manjunath Pujar and Monica R Mundada} }

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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Web mining: a survey of current research, techniques, and software.

• QINGYU ZHANG  and 
• RICHARD S. SEGALL

Department of Computer & Information Technology, Arkansas State University, State University, Arkansas 72467-0130, USA

Corresponding author.

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The purpose of this paper is to provide a more current evaluation and update of web mining research and techniques available. Current advances in each of the three different types of web mining are reviewed in the categories of web content mining, web usage mining, and web structure mining. For each tabulated research work, we examine such key issues as web mining process, methods/techniques, applications, data sources, and software used. Unlike previous investigators, we divide web mining processes into the following five subtasks: (1) resource finding and retrieving, (2) information selection and preprocessing, (3) patterns analysis and recognition, (4) validation and interpretation, and (5) visualization. This paper also reports the comparisons and summaries of selected software for web mining. The web mining software selected for discussion and comparison in this paper are SPSS Clementine, Megaputer PolyAnalyst, ClickTracks by web analytics, and QL2 by QL2 Software Inc. Applications of these selected web mining software to available data sets are discussed together with abundant presentations of screen shots, as well as conclusions and future directions of the research.

• web content mining
• web usage mining
• web structure mining
• web mining software
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A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. © Copyright 2024 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.

• DOI: 10.18535/IJECS/V6I1.29
• Corpus ID: 63876488

A Study on Web Content Mining

• Anurag Kumar
• Published 12 January 2017
• Computer Science
• International Journal of Engineering and Computer Science

13 Citations

A survey on web mining techniques, data mining: web data mining techniques, tools and algorithms: an overview.

• Highly Influenced

Web Mining for Information Retrieval

Web mining algorithms, a survey on web mining: techniques and applications, a systematic review web content mining tools and its applications, web page ranking using web mining techniques: a comprehensive survey, content based page ranking by using some natural language processing techniques, web content outlier mining using machine learning and mathematical approaches, semantic web mining for content-based online shopping recommender systems, 16 references, web mining: information and pattern discovery on the world wide web, web content mining techniques-a comprehensive survey, web mining research: a survey, web content mining tools: a comparative study, web mining: a survey of current research, techniques, and software, overview of visualization tools for web browser history data, data mining: introductory and advanced topics, authoritative sources in a hyperlinked environment, related papers.

Showing 1 through 3 of 0 Related Papers

Current Journal of Applied Science and Technology

Published: 2023-08-14

DOI: 10.9734/cjast/2023/v42i244179

Page: 32-42

Issue: 2023 - Volume 42 [Issue 24]

Review Article

Exploring the Landscape of Web Data Mining: An In-depth Research Analysis

Laxmi Choudhary *

Computer Science, Sabarmati University, Ahmedabad, India.

Shashank Swami

Department of Computer Science, Sabarmati University, Ahmedabad, India.

*Author to whom correspondence should be addressed.

The exponential growth of Web services and Web-based applications has led to an enormous volume of data, providing a rich source for mining valuable insights. Web mining differs from traditional data mining due to the unique nature of the data it handles. Web data exists in diverse forms, including web server logs, news pages, and hyperlinks. As the usage of the internet continues to surge, web mining has become essential to extract meaningful information and patterns from these varied data sources. Traditional data mining methods may not be directly applicable to web data due to its unstructured and heterogeneous nature. Web server logs contain valuable information about user interactions, click-streams, and user preferences, which can be mined to understand user behavior and improve website performance. News pages and other forms of web content are valuable sources for sentiment analysis, topic modeling, and information retrieval, helping businesses and researchers gain insights into public opinions and trends. Additionally, web structure mining deals with the analysis of hyperlinks, enabling the discovery of relationships between web pages and identifying authoritative sources. The continuous growth of web-based data necessitates the use of specialized methods in web mining to effectively extract knowledge and valuable patterns. Researchers and practitioners in this field are constantly exploring innovative techniques to make sense of the vast amount of data available on the World Wide Web. The paper provides web mining techniques on web data and presenting the latest advancements, researchers and practitioners can gain insights into the state of the field and identify potential areas for further exploration. This paper also reports the comparisons and summary of various methods of web data mining with applications, which gives the overview of development in research and some important research issues.

Keywords: Information retrieval, semantic web, text mining, web crawling, web mining, web content mining, web data mining, web structure mining, web usage mining

How to Cite

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