Data Mining the Web

Markov, Zdravko; Larose, Daniel T.

doi:10.1002/0470108096

Cited by 76 publications

(10 citation statements)

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“…Besides that there are two formats, which is Common Log File and Extended Common Log File Format, The common log format (CLF or "clog") is supported by a variety of web server applications and includes the following seven fields: If a field is unused in a particular entry dash "-" marks the omitted field. [6] The extended common log format (ECLF) is a variation of the common log format, formed by appending two additional fields onto the end of the record, the referrer field, and the user agent field [7].…”

Section: Figure 1-sample Of Server Log Filementioning

confidence: 99%

Identifying of User Behavior from Server Log File

2017

ijs

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Due to the increased of information existing on the World Wide Web (WWW), the subject of how to extract new and useful knowledge from the log file has gained big interest among researchers in data mining and knowledge discovery topics.Web miming, which is a subset of data mining divided into three particular ways, web content mining, web structure mining, web usage mining. This paper is interested in server log file, which is belonging to the third category (web usage mining). This file will be analyzed according to the suggested algorithm to extract the behavior of the user. Knowing the behavior is coming from knowing the complete path which is taken from the specific user.Extracting these types of knowledge required many of KDD (Knowledge Discovery in Database) steps such as preprocessing, pattern discovery, and pattern analysis. After that, the complete graph of the visited web will be drawn. The knowledge discussed in this paper, helps the web designers to improve their web site design and helps to improve their website usability and visitor's browsing experience by determining related link connections in the website.

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Section: Figure 1-sample Of Server Log Filementioning

confidence: 99%

Identifying of User Behavior from Server Log File

2017

ijs

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“…They suggest using concept learning methods to generate unique descriptions of sets of web content. This can be used to find the similar qualities of a new web page or a document [8]. In order to group or cluster the web content several techniques like Hierarchical Agglomerative Clustering, k-Means Clustering and Probability-Based Clustering can be used.…”

Section: Web Data Mining Frameworkmentioning

confidence: 99%

“…In order to group or cluster the web content several techniques like Hierarchical Agglomerative Clustering, k-Means Clustering and Probability-Based Clustering can be used. To evaluate the data models generated by the clusters Similarity based criterion functions, Probabilistic criterion function, MDL based model and Feature evaluations can be used [8].…”

Section: Web Data Mining Frameworkmentioning

confidence: 99%

Framework for Social Network Data Mining

Fernando¹,

Gapar²,

MdJohar³

2015

IJCA

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Social networks have become a vital component in personal life. People are addicted to social network features, updating their profile page and collaborating virtually with other members have become daily routines. Social networks contain massive collection of data. Web data mining is a new trend in the current research body. This conceptual paper introduces a framework that can be used to mine social network data. The proposed framework tries to handle the major limitations in current web mining frameworks by handling the unstructured and dynamic behavior of web data. Framework adopts the Hidden Markov Model to the data mining algorithm to predict the next status of web data.

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“…Web content mining has two perspectives: 1-Data Recovery, which aims to improve the process of searching or filtering the data to the users and 2-Database formation which aims to provide a model of Web data by integrate them. Web content mining includes various activities such as [9], [10], [11], [12], [15]:…”

Section: Web Content Miningmentioning

confidence: 99%

“…2-The Marco model which is actually a Marco chain of rank M that changing the state of a system depends to the current state and the M-1 previous states. Web structure mining applications includes determining quality associated with a subject, page classification, Web navigation, finding Web communities, adaptive Web sites design and personalization [9], [10], [11], [12].…”

Section: Web Structure Miningmentioning

confidence: 99%

PSEFiL: A Personalized Search Engine with Filtered Links

Aghaiipour-chafuchahi¹,

Ahmadi-Abkenari²

2015

IJCA

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The dynamic nature of the World Wide Web and its growing dimension make retrieving the exact information a difficult task. Inaccurate answers delivered by search engines especially for the query phrases with different meaning makes the feeling of dissatisfaction in today's surfers who needs the specific answer for their information demand. Search engines nowadays tries to understand users' request through studying his/her search background or even make users participate in the search process in order to clarify what he/she really needs. This trend is part of the search engines' endeavors to become personalized.

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Data Mining the Web

Cited by 76 publications

References 0 publications

Identifying of User Behavior from Server Log File

Identifying of User Behavior from Server Log File

Framework for Social Network Data Mining

PSEFiL: A Personalized Search Engine with Filtered Links

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