web mining research papers 2019 pdf

• Web Data Mining

Exploring Hyperlinks, Contents, and Usage Data

• © 2011
• Latest edition

Dept. Computer Science, University of Illinois, Chicago, Chicago, USA

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• Covers all key tasks and techniques of Web search and Web mining, i.e., structure mining, content mining, and usage mining
• Includes major algorithms from data mining, machine learning, information retrieval and text processing, which are crucial for many Web mining tasks
• Contains a rich blend of theory and practice, addressing seminal research ideas and also looking at the technology from a practical point of view
• Second edition includes new/revised sections on supervised learning, opinion mining and sentiment analysis, recommender systems and collaborative filtering, and query log mining
• Ideally suited for classes on data mining, Web mining, Web search, and knowledge discovery in data bases
• Provides internet support with lecture slides and project problems
• Includes supplementary material: sn.pub/extras

Part of the book series: Data-Centric Systems and Applications (DCSA)

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About this book

Liu has written a comprehensive text on Web mining, which consists of two parts. The first part covers the data mining and machine learning foundations, where all the essential concepts and algorithms of data mining and machine learning are presented. The second part covers the key topics of Web mining, where Web crawling, search, social network analysis, structured data extraction, information integration, opinion mining and sentiment analysis, Web usage mining, query log mining, computational advertising, and recommender systems are all treated both in breadth and in depth. His book thus brings all the related concepts and algorithms together to form an authoritative and coherent text. 

The book offers a rich blend of theory and practice. It is suitable for students, researchers and practitioners interested in Web mining and data mining both as a learning text and as a reference book. Professors can readily use it for classes on data mining, Web mining, and text mining. Additional teaching materials such as lecture slides, datasets, and implemented algorithms are available online.

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Text and Web Content Mining: A Systematic Review

Web Usage Mining—Process, Tools and Practices

Text Mining: The New Data Mining Frontier

Information integration.

• Information Retrieval
• Machine Learning
• Opinion Mining
• Pattern Mining
• Recommender Systems
• Schema Matching
• Semi-Supervised Learning

Social Network Analysis

• Structured Data Extraction

Unsupervised Learning

Web crawling.

• Web Link Analysis

Web Usage Mining

• Wrapper Generation

Table of contents (12 chapters)

Front matter, introduction, data mining foundations, association rules and sequential patterns, supervised learning, partially supervised learning.

• Bing Liu, Wee Sun Lee

Information Retrieval and Web Search

• Bing Liu, Filippo Menczer

Structured Data Extraction: Wrapper Generation

Opinion mining and sentiment analysis.

• Bing Liu, Bamshad Mobasher, Olfa Nasraoui

Back Matter

From the reviews:

"This is a textbook about data mining and its application to the Web. […] Liu succeeds in helping readers appreciate the key role that data mining and machine learning play in Web applications. […] It also motivates the student by adding immediacy and relevance to the concepts and algorithms described. I liked the way the concepts are introduced in a stepwise manner. […] I also appreciated the bibliographical notes at the end of each chapter." ACM Computing Reviews, W. Hu, , January 2009

From the reviews of the second edition:

“Liu (Univ. of Illinois, Chicago) discusses all three types of Web mining--structure, content, and usage--in the technology’s efforts to glean information from hyperlinks, Web page content, and usage logs. […] Practical examples complement the discussions throughout the text, and each chapter includes useful ‘Bibliographic Notes’ and an extensive bibliography. […] Liu states that his intended audience includes bothundergraduate and graduate students, but notes that researchers and Web programmers could benefit from this text as well. Summing Up: Recommended. Upper-division undergraduates through professionals.” J. Johnson, Choice, Vol. 49 (5), January 2012

"[...] Liu's book provides a comprehensive, self-contained introduction to the major data mining techniques and their use in Web data mining. [...] Professionals and researchers alike will find this excellent book handy as a reference. Its extensive lists of references at the end of each chapter provide hundreds of pointers for further reading. As a textbook, it is also suitable for advanced undergraduate and graduate courses on Web mining; it is highly selfcontained and includes many easy-to-understand examples that will help readers grasp the key ideas behind current Web data mining techniques." ACM Computing Reviews, Fernando Berzal, February 2012

Authors and Affiliations

About the author.

Bing Liu is a professor of Computer Science at the University of Illinois at Chicago (UIC). He received his PhD in Artificial Intelligence from the University of Edinburgh. Before joining UIC, he was with the National University of Singapore. His current research interests include opinion mining and sentiment analysis, text and Web mining, data mining, and machine learning. He has published extensively in top journals and conferences in these fields. Several of his publications are considered seminal papers of the fields and are highly cited. He has also given more than 30 keynote and invited talks in academia and in industry. On professional services, Liu has served as associate editors of IEEE Transactions on Knowledge and Data Engineering (TKDE), Journal of Data Mining and Knowledge Discovery (DMKD), and SIGKDD Explorations, and is on the editorial boards of several other journals. He has also served as program chairs of IEEE International Conference on Data Mining (ICDM-2010), ACM Conference on Web Search and Data Mining (WSDM-2010), ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2008), SIAM Conference on Data Mining (SDM-2007), ACM Conference on Information and Knowledge Management (CIKM-2006), and Pacific Asia Conference on Data Mining (PAKDD-2002). Additionally, Liu has served extensively as area chairs and program committee members of leading conferences on data mining, Web mining, natural language processing, and machine learning. More information about him can be found from http://www.cs.uic.edu/~liub.

Bibliographic Information

Book Title : Web Data Mining

Book Subtitle : Exploring Hyperlinks, Contents, and Usage Data

Authors : Bing Liu

Series Title : Data-Centric Systems and Applications

DOI : https://doi.org/10.1007/978-3-642-19460-3

Publisher : Springer Berlin, Heidelberg

eBook Packages : Computer Science , Computer Science (R0)

Copyright Information : Springer-Verlag GmbH Germany, part of Springer Nature 2011

Hardcover ISBN : 978-3-642-19459-7 Published: 26 June 2011

Softcover ISBN : 978-3-642-26891-5 Published: 03 August 2013

eBook ISBN : 978-3-642-19460-3 Published: 25 June 2011

Series ISSN : 2197-9723

Series E-ISSN : 2197-974X

Edition Number : 2

Number of Pages : XX, 624

Topics : Information Storage and Retrieval , Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences , Data Mining and Knowledge Discovery , Pattern Recognition , Artificial Intelligence

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• Corpus ID: 18032585

Web Mining - Concepts, Applications & Research Directions

• J. Srivastava , P. Desikan , Vipin Kumar
• Computer Science

Figures from this paper

17 Citations

A review on web mining, analysis of web mining technology and their impact on semantic web.

• Highly Influenced

A Lime Light on the Emerging Trends of Web Mining

A guesstimate on web usage mining algorithms and techniques, a hand to hand taxonomical survey on web mining, a survey: techniques of an efficient search annotation based on web content mining, see blockindiscussions, blockinstats, blockinand blockinauthor blockinprofiles blockinfor blockinthis blockinpublication web blockinmining blockinto blockincreate blockinsemantic blockincontent: blockina case blockinstudy blockinfor blockinthe blockinenvironment, web mining to create semantic content: a case study for the environment, web usage mining for automatic link generation, survey of web content mining and relation extraction techniques, 62 references, web mining: information and pattern discovery on the world wide web, web usage mining: discovery and applications of usage patterns from web data, web mining research: a survey, data preparation for mining world wide web browsing patterns, data mining on the web, mining e-commerce data: the good, the bad, and the ugly, adaptive web sites: conceptual cluster mining, the world-wide web: quagmire or gold mine, the anatomy of a large-scale hypertextual web search engine, a belief-driven method for discovering unexpected patterns, 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

• Download Citation
• Endnote/Zotero/Mendeley (RIS)

Margaret H. Dunham, ―Data Mining Introductory & Advanced Topics‖, Pearson Education.

Mustafa Ali Bamboat, Ghulam Sarfaraz Khan, Naadiya Mirbahar, Sheeba Memon, “Web Content Mining Techniques for Structured Data: A Review” ( SJHSE) Sindh Journal of Headways in Software Engineering. 2022;1(1).

Richlin Selina Jebakumari A. Nancy Jasmine Goldena. A Survey on Web Content Mining Methods and Applications for Perfect Catch Responses. International Research Journal of Engineering and Technology (IRJET). 2019;06(01): 407-412. e-ISSN: 2395-0056 p-ISSN: 2395-0072.

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Available: https://doi.org/10.1088/1757- 899x/1022/1/012018.

Andemariam Mebrahtu, Balu Srinivasulu. Web Content Mining Techniques and Tools;2017. IJSCMC

URL: https://www.ijcsmc.com/docs/papers/April2017/V6I4201725.pdf

Zhang H, Chen Z, Li M, Su Z. Relevance feedback and learning in content-based image search, World Wide Web. 2003;6(2):131–155.

Anil B. Pawar, Madhuri A. Jawale, Chaitanya P. Kale. A Powerful Techniques and Applications of Web Mining” in Intelligent Systems and Computer Technology. 269-276. DOI:10.3233/APC200153.

Wang Bo, Xu Jing. Research on Web Data Mining Hadoop Simulation Platform Based on Cloud Computing", Electronic Design Engineering. 2018;26(2): 22-25.

Chen L, Lian W, Chue W. Using web structure and summarization techniques for web content mining, Inform. Process. Management: Int. J. 2005;41(5):1225–1242.

Kavita, Mahani P, Ruhil N. Web data mining: A perspective of research issues and challenges. In international conference on computing for sustainable global development. 2016;3235-8. IEEE.

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Ramakrishna, Gowdar et al. Web Mining: Key Accomplishments, Applications and Future Directions”, in the International Conference on Data Storage and Data Engineering; 2010.

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Nacim Fateh Chikhi, Bernard Rothenburger, Nathalie Aussenac-Gilles. A Comparison of Dimensionality Reduction Techniques for Web Structure Mining. Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence. 2007;116-119.

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Use of web mining in studying innovation

2014, Scientometrics

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