期刊名称：International Journal of Computer Trends and Technology
电子版ISSN：2231-2803
出版年度：2013
卷号：4
期号：8-3
出版社：Seventh Sense Research Group
摘要：Data mining is an interdisciplinary subfield of computer science and it is the computational process of discovering patterns in large data sets. The overall goal of the data mining process is to extract information from a dataset and transform it into an understandable structure for further use. Presuming is a technique to assume a future behavior depending on their past behavior. Presuming is a very integral and vital part of Data mining. In ECommerce application, there are mainly three important parts, first Business Owner, second customer and third Items. Owner earns profit by selling items to customer. It is well known fact from MarketBasket Analysis of Data Mining that Items having associability with each other, and that can be found by mining information from customer buyed products. This analysis helps in finding the item associability. Here we are going to propose a analysis by using which we effectively and easily get the item association and can make the prediction for item which is missing in that transaction. For this we are using Association rule and Apriori Algorithm, in order to reduce the rule mining cost, this algorithm is generating frequent item sets from the transactions which are already done. This algorithm uses Boolean vector with relational AND operation to discover frequent item sets and generate the association rule.
关键词：Association Rule Mining; Presuming; Data mining; association
