Web mining research: a survey

By R. Kosala, Hendrik Blockeel. 2000

1,913 citations recorded by the source corpus.

With the huge amount of information available online, the World Wide Web is a fertile area for data mining research. The Web mining research is at the cross road of research from several research communities, such as database, information retrieval, and within AI, especially the sub-areas of machine learning and natural language processing. However, there is a lot of confusions when comparing research efforts from different point of views. In this paper, we survey the research in the area of Web mining, point out some confusions regarded the usage of the term Web mining and suggest three Web mining categories. Then we situate some of the research with respect to these three categories. We also explore the connection between the Web mining categories and the related agent paradigm. For the survey, we focus on representation issues, on the process, on the learning algorithm, and on the application of the recent works as the criteria. We conclude the paper with some research issues.

Subfields: Artificial Intelligence, Information Systems, Statistical and Nonlinear Physics

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Citations in the corpus

Natural Language Processing for EHR-Based Computational Phenotyping (2018, background): “text mining, and [25] is a survey for web mining.”
A survey of the applications of text mining in financial domain (2016, background): “Kosala and Blockeel (2000) presented a survey paper on web mining.”
Feature Coding in Image Classification: A Comprehensive Study (2014, neutral): “image and video retrieval [2], web content analysis [3],”
Measuring Prices and Price Competition Online: Amazon and Barnes and Noble (2002, neutral): “Finally, this project provides a contribution to the growing field of web mining (see Kosala and Blockeel, 2000 for a survey).”
Weighted PageRank algorithm (2004, background): “Web mining consists of Web Content Mining (WCM), Web Structure Mining (WSM), and Web Usage Mining (WUM) [6] [7] [9].”
Hierarchical Document Clustering using Frequent Itemsets (2003, background): “Document clustering has been studied intensively because of its wide applicability in areas such as web mining [30, 22], information retrieval [45], and topological analysis.”
#Earthquake: Twitter as a Distributed Sensor System (2013, methodology): “Harvesting information from social media feeds is in essence a web-mining process (Kosala and Blockeel 2000, Sakaki et al. 2010, Russell 2011).”
Mining interesting knowledge from weblogs: a survey (2005, background): “In contrast with [2,7,9], we focus only on Web Usage Mining, specifically on the research results reported in the literature since 2000 and on the software currently available.”
Monitoring web browsing behavior with differential privacy (2014, background): “• finding the most popular web pages and how the trend changes over time [9, 19],”
Capturing value from big data – a taxonomy of data-driven business models used by start-up firms (2016, background): “Several papers from the Information System field, such as data mining and data warehousing, list – mostly non-comprehensively and non-structured – potential data sources or types (Singh and Singh, 2010; Han et al., 2011; Schroeck et al., 2012; Kosala and Blockeel, 2000).”
Frequent pattern mining: current status and future directions (2007, methodology): “As a general data structure, graphs have become increasingly important in modeling sophisticated structures and their interactions, with broad applications including chemical informatics, bioinformatics, computer vision, video indexing, text retrieval, and Web analysis.”
A Descriptive Framework for the Field of Data Mining and Knowledge Discovery (2008, neutral): “Web data combine text documents, hypertext documents, links structures, and logs (server logs and browser logs).(58) Concepts in Web mining task fit into the three categories proposed:(58) Web content mining, Web structure mining, and Web usage mining.”
Social Media Mining: An Introduction (2014, methodology): “Other similar techniques to PageRank for computing influential nodes in a webgraph, such as the HITS [Kleinberg, 1998] algorithm, can be found in [Chakrabarti, 2003; Kosala and Blockeel, 2000].”