RESEARCH ARTICLE OPEN ACCESS Design and Development of Data Mining System to Analyze Cars using Improved ID3 with TkNN Clustering Algorithm

Conventional way of business is a challenging in car market due to many competitors are there around the world for providing competitive products. The car manufacturers categorizes the car users and have to invent a suitable car; the seller correctly groups the buyers and he sells a right car; and the customers selects best car by analyzing more brands of cars with ‘N ’ number of sellers. These three cases they spent too much of time for analyzing old or statistical data for choosing a right product. Now a day’s customers are required comfort and their loving brand & color. With the advent of the Internet and Data Mining Algorithms has undoubtedly contributed to the shift of marketing focus. In this paper, we proposed Improved ID3 with TkNN algorithm for best car market analysis. We have executed the same in WEKA Tool with Java code. We analyzed the graphical performance analysis between TkNN and our novel improved ID3 with TkNN clustering algorithms with Classes to Clusters evaluation purchase, safety, luggage booting, persons (seating capacity), doors, maintenance and buying attributes of customer’s requirements for unacceptable/acceptable/good/very good ratings of a car to purchase