An Improved Web Mining Technique to Fetch Web Data Using Apriori and Decision Tree

World Wide Web is the largest source of information. Most of the data on the web is dynamic and is in unstructured form. It is becoming difficult to get the relevant data from the web. Data Mining is the field of computer science which is used to extract knowledge from very large amount of data. Web mining is the application of data mining, which implements various techniques of data mining to get the efficient knowledge from the web data. In past time, most of the websites were developed using HTML but HTML has many limitations like limited tags, not case sensitive and designed to display data only, Web developers has now started to develop Web pages on emerging Web Technologies like XML, Flash etc. XML was designed to describe data and to focus on what the data is. XML also plays the role of a metalanguage and allows authors to create customized markup language for different types of documents, making it a standard data format for online data exchange. To date, famous algorithms like Apriori and FPGrowth algorithms are used to fetch the web data for XML contents. In the proposed paper, a hybrid approach is used to fetch HTML as well as XML contents from a web page. In the hybrid approach, Apriori algorithm is used to remove the unimportant information from the contents and Decision tree is used to fetch the contents from a web page. Various factors like execution time, precision, recall and f-measure and gmeasure are calculated.