Data Mining- World Wide Web

Over the last few years, the World Wide Web has become a significant source of information and simultaneously a popular platform for business. Web mining can define as the method of utilizing data mining techniques and algorithms to extract useful information directly from the web, such as Web documents and services, hyperlinks, Web content, and server logs. The World Wide Web contains a large amount of data that provides a rich source to data mining. The objective of Web mining is to look for patterns in Web data by collecting and examining data in order to gain insights.

What is Web Mining?

Web mining can widely be seen as the application of adapted data mining techniques to the web, whereas data mining is defined as the application of the algorithm to discover patterns on mostly structured data embedded into a knowledge discovery process. Web mining has a distinctive property to provide a set of various data types. The web has multiple aspects that yield different approaches for the mining process, such as web pages consist of text, web pages are linked via hyperlinks, and user activity can be monitored via web server logs. These three features lead to the differentiation between the three areas are web content mining, web structure mining, web usage mining.

There are three types of data mining:

1. Web Content Mining:

Web content mining can be used to extract useful data, information, knowledge from the web page content. In web content mining, each web page is considered as an individual document. The individual can take advantage of the semi-structured nature of web pages, as HTML provides information that concerns not only the layout but also logical structure. The primary task of content mining is data extraction, where structured data is extracted from unstructured websites. The objective is to facilitate data aggregation over various web sites by using the extracted structured data. Web content mining can be utilized to distinguish topics on the web. For Example, if any user searches for a specific task on the search engine, then the user will get a list of suggestions.

2. Web Structured Mining:

The web structure mining can be used to find the link structure of hyperlink. It is used to identify that data either link the web pages or direct link network. In Web Structure Mining, an individual considers the web as a directed graph, with the web pages being the vertices that are associated with hyperlinks. The most important application in this regard is the Google search engine, which estimates the ranking of its outcomes primarily with the PageRank algorithm. It characterizes a page to be exceptionally relevant when frequently connected by other highly related pages. Structure and content mining methodologies are usually combined. For example, web structured mining can be beneficial to organizations to regulate the network between two commercial sites.

3. Web Usage Mining:

Web usage mining is used to extract useful data, information, knowledge from the weblog records, and assists in recognizing the user access patterns for web pages. In Mining, the usage of web resources, the individual is thinking about records of requests of visitors of a website, that are often collected as web server logs. While the content and structure of the collection of web pages follow the intentions of the authors of the pages, the individual requests demonstrate how the consumers see these pages. Web usage mining may disclose relationships that were not proposed by the creator of the pages.

Some of the methods to identify and analyze the web usage patterns are given below:

I. Session and visitor analysis:

The analysis of preprocessed data can be accomplished in session analysis, which incorporates the guest records, days, time, sessions, etc. This data can be utilized to analyze the visitor's behavior.

The document is created after this analysis, which contains the details of repeatedly visited web pages, common entry, and exit.

II. OLAP (Online Analytical Processing):

OLAP accomplishes a multidimensional analysis of advanced data.

OLAP can be accomplished on various parts of log related data in a specific period.

OLAP tools can be used to infer important business intelligence metrics

Challenges in Web Mining:

The web pretends incredible challenges for resources, and knowledge discovery based on the following observations:

• The complexity of web pages:

The site pages don't have a unifying structure. They are extremely complicated as compared to traditional text documents. There are enormous amounts of documents in the digital library of the web. These libraries are not organized according to a specific order.

• The web is a dynamic data source:

The data on the internet is quickly updated. For example, news, climate, shopping, financial news, sports, and so on.

• Diversity of client networks:

The client network on the web is quickly expanding. These clients have different interests, backgrounds, and usage purposes. There are over a hundred million workstations that are associated with the internet and still increasing tremendously.

• Relevancy of data:

It is considered that a specific person is generally concerned about a small portion of the web, while the rest of the segment of the web contains the data that is not familiar to the user and may lead to unwanted results.

• The web is too broad:

The size of the web is tremendous and rapidly increasing. It appears that the web is too huge for data warehousing and data mining.

Mining the Web's Link Structures to recognize Authoritative Web Pages:

The web comprises of pages as well as hyperlinks indicating from one to another page. When a creator of a Web page creates a hyperlink showing another Web page, this can be considered as the creator's authorization of the other page. The unified authorization of a given page by various creators on the web may indicate the significance of the page and may naturally prompt the discovery of authoritative web pages. The web linkage data provide rich data about the relevance, the quality, and structure of the web's content, and thus is a rich source of web mining.

Application of Web Mining:

Web mining has an extensive application because of various uses of the web. The list of some applications of web mining is given below.

• Marketing and conversion tool
• Data analysis on website and application accomplishment.
• Audience behavior analysis
• Advertising and campaign accomplishment analysis.
• Testing and analysis of a site.