Clustering Web Information Sources

Vakali, Athena; Pallis, George; Angelis, Lefteris

doi:10.4018/978-1-59904-510-8.ch005

Cited by 5 publications

(13 citation statements)

References 48 publications

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“…Clustering is a technique that used to group similar objects, Web environments include several Web contents, which clustered based on certain characteristics or parameters for example Web pages clustering. Clustering Web can be one of the following types [12,14].…”

Section: ) Clusteringmentioning

confidence: 99%

“…Web pages are grouping based on such related contents, this information is useful in search engines and information retrieval approaches, which increased the accessibility and develop content, based delivering applications [14].…”

Section: ) Clusteringmentioning

confidence: 99%

“…3) Web Page Content Mining (WPCM): WPCM is a mining process of the entire content of Web pages by search engines to ranked Web pages through searching process, this process includes extracting structured data, then list the result based on its correlation [14,22].…”

Section: B Structured Data Techniquesmentioning

confidence: 99%

“…Including the techniques that used to extract data combined from heterogeneous sources such as Web page in database, books and Author's which have been written in semi-structured forms, these techniques includes the following [12,14,22]: 1) Object Exchange Model (OEM): Defined as the process of extraction useful information from semistructured contents, then segment similar content into coherent groups [14].…”

Section: Semi-structured Data Techniquesmentioning

confidence: 99%

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A Survey on Web Mining Techniques and Applications

Al-Asadi¹,

Park²,

Ramli³

2017

International Journal on Advanced Science, Engineering and Information Technology

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Information on Internet and especially on Web sites increasing rapidly day by day, Web sites play an important role in this manner where a lot of Web users are always upload, download and brows a lot of contents based on their needs. Web server provide a way to browse these contents that scattered in many Web sites that reside in it by assigning an Internet Protocol (IP) address or Domain Name System (DNS) to be accessed around the world. Server log the requests that made by users to access their interesting content then store this information in the form of log file. Log file size can be growth from some kilobytes to several megabytes in few days depending on data traffic and the popularity of Web sites. With the fast growth of the data and information in Web environment made a necessity to use sophisticated techniques that have never used in other domains to extract knowledge and significant Web patterns. Web Mining is an extension of Data Mining that integrated various technology in research fields including Artificial Intelligence (AI), statistics, informatics, knowledge discovery and computational linguistics. The aim of Web Mining is to provide a algorithm or technique to make data accesses more efficient and convenient. Web Mining techniques are categorized into three classes depend on which part to be mined which are: Web Content Mining (WCM), Web Structure Mining (WSM) and Web Usage Mining (WSM), in this paper a survey of Web Mining techniques and application are discussed briefly.

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Section: ) Clusteringmentioning

confidence: 99%

Section: ) Clusteringmentioning

confidence: 99%

Section: B Structured Data Techniquesmentioning

confidence: 99%

Section: Semi-structured Data Techniquesmentioning

confidence: 99%

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A Survey on Web Mining Techniques and Applications

Al-Asadi¹,

Park²,

Ramli³

2017

International Journal on Advanced Science, Engineering and Information Technology

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“…The Web structure mining attempts to discover the model underlying the link structure of the Web. The Web usage mining attempts to discover knowledge for the data generated by the Web surfer's sessions or behaviors [11].…”

Section: Web User Clusteringmentioning

confidence: 99%

Web Users Clustering Based on Fuzzy C-MEANS

Ali

Alrabighi

2016

VAWKUM trans. comput. sci.

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The Web contributes greatly to our life in many fields such as education, entertainment, Internet banking, online shopping and software downloading. This has led to rapid growth in the number of Internet users, which resulting in an explosive increase in traffic or bottleneck over the Internet performance. This paper proposes a new approach to group users according to their Web access patterns. The proposed approach for grouping users is based on Fuzzy c-means technique, which allows web users to be assigned into more than one cluster or interest. Each web user has a degree of membership of belonging to each cluster. The experimental results showed that the web users were successfully clustered to similar groups very fast using Fuzzy-c-means. In addition, the Fuzzy-c-means performed well and became much better when the clusters number increased on two real Bo2 and NY datasets. The proposed intelligent web users clustering based on Fuzzy-c-means can be used for discovering users' interests in Web pages that can contribute in enhancing several approaches such as Web caching, Web pre-fetching and Web recommender systems that are recently used to improve the Web performance.

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A new approach to web users clustering and validation: a divergence‐based scheme

Koutsonikola

Petridou

Vakali

et al. 2009

International Journal of Web Information Systems

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Purpose-Web users' clustering is an important mining task since it contributes in identifying usage patterns, a beneficial task for a wide range of applications that rely on the web. The purpose of this paper is to examine the usage of Kullback-Leibler (KL) divergence, an information theoretic distance, as an alternative option for measuring distances in web users clustering. Design/methodology/approach-KL-divergence is compared with other well-known distance measures and clustering results are evaluated using a criterion function, validity indices, and graphical representations. Furthermore, the impact of noise (i.e. occasional or mistaken page visits) is evaluated, since it is imperative to assess whether a clustering process exhibits tolerance in noisy environments such as the web. Findings-The proposed KL clustering approach is of similar performance when compared with other distance measures under both synthetic and real data workloads. Moreover, imposing extra noise on real data, the approach shows minimum deterioration among most of the other conventional distance measures. Practical implications-The experimental results show that a probabilistic measure such as KL-divergence has proven to be quite efficient in noisy environments and thus constitute a good alternative, the web users clustering problem. Originality/value-This work is inspired by the usage of divergence in clustering of biological data and it is introduced by the authors in the area of web clustering. According to the experimental results presented in this paper, KL-divergence can be considered as a good alternative for measuring distances in noisy environments such as the web.

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Clustering Web Information Sources

Cited by 5 publications

References 48 publications

A Survey on Web Mining Techniques and Applications

A Survey on Web Mining Techniques and Applications

Web Users Clustering Based on Fuzzy C-MEANS

A new approach to web users clustering and validation: a divergence‐based scheme

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