• DocumentCode
    161050
  • Title

    Enhanced data processing using positive negative association mining on AJAX data

  • Author

    Doshi, Montu ; Roy, Bidisha

  • Author_Institution
    St. Francis Inst. of Technol., Mumbai Univ., Mumbai, India
  • fYear
    2014
  • fDate
    4-5 April 2014
  • Firstpage
    386
  • Lastpage
    390
  • Abstract
    Knowledge discovery is the process of analyzing data from different perspectives and summarizing it into useful information. Association rule mining is a data mining process used widely in traditional databases to find the positive association rules. Association rules are created by analyzing data for frequent patterns and by using the criteria support and confidence to identify the most important relationships. However, there are some other challenging rule mining topics like negative association rule mining. In this research, a rule mining approach has been proposed that provides efficient and secure solution using positive and negative association rule computation on Asynchronous JavaScript and XML (AJAX) data. By using AJAX, we get the search result in the form of semantic data. Whenever data search from database is intended, the next possible word of the search will be made available.
  • Keywords
    Java; XML; data analysis; data mining; AJAX data; asynchronous JavaScript and XML data; data analysis process; data mining process; data search; enhanced data processing; frequent pattern mining; knowledge discovery; positive negative association rule mining; Algorithm design and analysis; Association rules; Data processing; Databases; Information technology; XML; AJAX; Apriori Algorithm; Association rule mining; Database; Frequent Pattern-Growth (FP-Growth) Algorithm; Horizontal Tree Approach;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Circuits, Systems, Communication and Information Technology Applications (CSCITA), 2014 International Conference on
  • Conference_Location
    Mumbai
  • Type

    conf

  • DOI
    10.1109/CSCITA.2014.6839292
  • Filename
    6839292