Web Mining: An Application of Data Mining

This paper is a work on survey on the existing techniques of web mining and the issues which are related to it. The WWW i.e. World Wide Web acts as an interactive and trendy way to transfer information. The enormous and diverse information is available on the web, although the end users cannot make use of the information very effectively and easily. Data mining concentrates on non trivial extraction of implicit previously unknown and potential useful information from the very large quantity of data. Web mining is one of the applications of data mining which has become an important area of research due to vast amount of World Wide Web services in recent years. The aim of this paper is to provide the past and current techniques in Web Mining. This paper also reports the summary of various techniques of web mining approached from the following angles like Feature Extraction, Transformation and Representation and Data Mining Techniques in various application domains. In this paper, we will discuss the research work done by different users depicting the pros and cons are discussed. We will also discuss the overview of growth in research of web mining and some important research issues related to it.

Computers promise that be as a repository of knowledge and wisdom, but instead, they sent us large amounts of data, web mining is the process of information discovery and knowledge from the Web data. The data is collected from the server, client, proxy server or database in Web mining. Web mining methods are divided into three categories: web content mining, web structure mining and web usage mining. There are several functional areas including e-commerce web mining, text mining, and management of customer behavior. Web mining research focuses on developing knowledge extraction techniques which are used for data analysis. 3 main methods that are used for data mining in web include: association or association rules, sequential patterns, and clustering requirements. The main objective of the web mining is to collect information about the user navigation patterns. Of course, web mining is faced with various challenges and constraints. And many researches are currently doing research in the field of web mining that aim to solve this problem.

This study presents the role of Web mining an explosive growth of the World Wide Web; websites are providing an information and knowledge to the end users. This is the review paper which show deep and intense study of various technologies available for web mining and it is the application of data mining techniques to extract knowledge from web. Current advances in each of the three different types of web mining are reviewed in the categories of web content mining, web usage mining, and web structure mining. Index Terms—web mining, web content mining, web usage mining, web structure mining.

World Wide Web has changing into one amongst the foremost comprehensive data resources. It most likely, if not perpetually, covers the data requirement for any user. However the net demonstrates several radical variations to traditional information containers such as databases in schema, volume, topic coherence etc. Web mining techniques could be applied to fully use web information in an effective and efficient manner, partially or completely. However, mining techniques are not the only tools to use web information efficiently but the mining techniques are the best solution. In this paper we study Web mining, Web mining categories and overview of various research issues and development efforts in web mining.

Web is a platforms for information exchange, as it is simple and easy to publish documents. Searching for information becomes a difficult and time-consuming process as the web grows. Web mining uses various data mining techniques to discover useful knowledge from usage log file from the web. The mining tools are used to scan the HTML documents, images, and text, the results is provided for the search engines.It can assist search engines in providing productive results of each search in order of their relevance. In this paper, we brief introduction to the concepts related to web mining and then an overview of different Web usage mining.

International Journal of Engineering Sciences & Research Technology, 2013

The hunt for knowledge has led to new discoveries and inventions. With the materialization of World Wide Web, it became a core for all these discoveries and inventions. Web browsers became a tool to make the information available at our finger tips. WWW stands for World Wide Web, and it is an advanced information retrieval system. As years passed World Wide Web became weighed down with information and it became hard to retrieve data according to the need. Web mining came as salvage for the above problem. Web content mining is a subdivision under web mining. This paper deals with a study of different techniques and pattern of content mining and the areas which has been influenced by content mining. The web contains structured, unstructured, semi structured and multimedia data. This survey focuses on how to apply content mining on the above data. It also points out how web content mining can be utilized in web usage mining.

Web is a collection of interrelated files on one or more web servers while web mining means extracting valuable information from web databases. Web mining is one of the data mining domains where data mining techniques are used for extracting information from the web servers. The web data includes web pages, web links, objects on the web and web logs. Web mining is used to understand the customer behaviour, evaluate a particular website based on the information which is stored in web log files. Web mining is evaluated by using data mining techniques, namely classification, clustering, and association rules. It has some beneficial areas or applications such as Electronic commerce, E-learning, E-government, E-policies, E-democracy, Electronic business, security, crime investigation and digital library. Retrieving the required web page from the web efficiently and effectively becomes a challenging task because web is made up of unstructured data, which delivers the large amount of information and increase the complexity of dealing information from different web service providers. The collection of information becomes very hard to find, extract, filter or evaluate the relevant information for the users. In this paper, we have studied the basic concepts of web mining, classification, processes and issues. In addition to this, this paper also analyzed the web mining research challenges.

International Journal on Advanced Science, Engineering and Information Technology

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.

The World Wide Web (WWW) is a huge source of data obtained either from the web content, represented by the billions of publicly available pages, or from the web usage represented by the log information daily collected by all the web servers around the world. Hence, there is an increasing necessity for the web users to utilize the automated tools for finding the desired information, due to the massive growth of the information sources available on the WWW. Web Mining serves as a prominent approach for the discovery of relationship between the web documents. This paper presented a review of the overview and taxonomy of the web mining and comparative study of the recent web mining techniques. It also includes the types of web mining and tools used in the web mining process.

Proceedings of Elsevier Second International Conference on Emerging Research in Computing, Information, Communication and Application (ERCICA-2014)

Nowadays the World Wide Web (commonly called as Web) is used widely and it has impacted on almost every facet of our lives. To search and retrieve the information from the web requires an effective and efficient technique as it has become a challenge due to expanding size and complexity of web. Web Mining tackles this problem by gathering useful information from web by using its three categories web structure mining, web content mining and web usage mining. In this paper discussion is done by explaining the area of WebMining, its categories and algorithms associated with it. The algorithms discussed are PageRank, SimRank, TF-IDF, k- nearest neighbour, PageGather and CDL4. Then we summarize the algorithms over parameters such as its working, input parameters, complexity and their pros and cons. Also we analyze discussed algorithms over the parameters: relevance, their technique and regression analysis.