Identifying Buying Preferences of Customers in Real Estate Industry Using Data Mining

Database mining is motivated by decision support problems faced by most business organizations and is described as an important area of research. One of the most challenges in database mining is developing fast and efficient algorithms that can handle large volume of data because most mining algorithms performs computation over the entire database and often the database are very large. The methodologies of data mining that are used by existing identifying buying preferences of customers in real estate industries are Apriori algorithm to find frequent item sets for a large database but it has many shortcoming which can be resolved by many other algorithms one of which is Partitioning the large database and using Apriori algorithm which is named as Improved Apriori Algorithm. We are using improved Apriori algorithm as it increases the throughput and reduces the memory that is being used for candidate set generation. Existing identifying buying preferences of customers in real estate industry display the frequent item set when searched for a particular item but the proposed system will show the results of prediction when searched for a particular item so as to improve the business throughput