ABSTRACT:A new algorithm for infrequent item set mining, finding infrequent weighted item set in transaction database. Frequent weighted item sets represent correlation regularly holding in data in which items may weight differently. The research society has focused on the infrequent weighted item set mining problem. Infrequent weighted item set discover item sets whose frequency of occurrence in the analyzed data is less than or equal to a maximum threshold. To discover infrequent weighted item set, two algorithms are discovered Infrequent weighted item set IWI and Minimal infrequent item set MIWI. In this survey is focused on the infrequent weighted item sets, from transactional weighted data sets to address IWI support measure is defined as a weighted frequency of occurrence of an item set in the analyzed data. Occurrence weights derived from the weights associated with items in each transaction and applying a given cost function.

Australian rules football is a vibrant sport, based on the principle of territorial foray. It adorns with both a large playing field and more number of participants predominantly athletes who are dynamic with multifaceted skills. The research consists of the score data analysis for the years 2012 to 2017. s.From the given score data of the 18 AFL playing teams, it was required to build severable working Massey Constant Rating models to come up with the structured report and understanding of the performance of the teams and interpretation various results from the analysis. Primarily, it was noted that the home advantage does have an impact on the well performing teams. Then the teams’ ratings were calculated based on the standard rating value of 100 to initiate our analysis and calculation to find the actual ratings.It reveals interesting observations about the teams’ performance based ratings and their consistency. Facts show critical aspects of the home advantage and the evenness of the competition for the past six year