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Outline

Title
Abstract
Figures
Introduction
Material and Method
Unstructured Data Techniques
Structured Data Techniques
Semi-Structured Data Techniques
Web Data Extraction Language
Multimedia Data Techniques
Results and Discussion
Future Direction
Conclusions
References

A Survey on Web Mining Techniques and Applications

Profile image of azizul azhar ramli (PhD - Soft Computing)azizul azhar ramli (PhD - Soft Computing)

International Journal on Advanced Science, Engineering and Information Technology

https://doi.org/10.18517/IJASEIT.7.4.2803
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7 pages

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Abstract

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.

Figures (3)
WEB MINING TECHNIQUES AND APPLICATION 3) Summarization: Is a technique that used to reduce the length of Web documents and give decision to the user if should read these documents (pages) or not instead of reading the first paragraph to know if the Web document is related or not to his interest [5]. TABLE I
WEB MINING TECHNIQUES AND APPLICATION 3) Summarization: Is a technique that used to reduce the length of Web documents and give decision to the user if should read these documents (pages) or not instead of reading the first paragraph to know if the Web document is related or not to his interest [5]. TABLE I
Fig.1. Web Content Mining Techniques and Applications
Fig.1. Web Content Mining Techniques and Applications
Fig.2. High level architecture of different Web services In WUM, data can be collected from different levels or obtain from an organization as dataset such as NASA, the data collected differ in terms of content structure, sources, type of information available, segment methods and implementation method. Data source levels of Web Usage Data categorized into: Client level data, Proxy level data and Server level data [10, 11], figure 2 shows the architecture level of Web data sources.
Fig.2. High level architecture of different Web services In WUM, data can be collected from different levels or obtain from an organization as dataset such as NASA, the data collected differ in terms of content structure, sources, type of information available, segment methods and implementation method. Data source levels of Web Usage Data categorized into: Client level data, Proxy level data and Server level data [10, 11], figure 2 shows the architecture level of Web data sources.

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