Improvised Apriori Algorithm using frequent pattern tree for real time applications in data mining

There are several mining algorithms which have been developed over the years. Apriori Algorithm is one of the most important algorithm which is used to extract frequent itemsets from large database and get the association rule for discovering the knowledge. It basically requires two important things: minimum support and minimum confidence. Firstly, we check whether the items are greater than or equal to the minimum support and we find the frequent itemsets respectively. Secondly, the minimum confidence constraint is used to form association rules. Based on this algorithm, this paper indicates the limitation of the original Apriori algorithm of wasting time and space for scanning the whole database searching on the frequent itemsets, and presents an improvement on Apriori by reducing that wasted time depending on scanning only some transactions by implementing a mathematical formula which initially partitions the set of transactions into clusters and select one particular cluster out of this. Our Algorithm can be used in the library for finding the book that is most frequently read and it can also be used in the grocery shop database by the shopkeeper for finding the itemsets which are frequently sold as this takes lesser time and it’s easy to find the items so that shopkeeper can make profit by getting the information of those items which are frequently sold. It gives this result only by using parallel algorithm. The code is implemented in java and the platform used is eclipse. This algorithm’s result is generated on Mac using parallel algorithm otherwise it would be similar to the results generated so far by many others. That is how the results are shown and the data structure used in this approach is the frequent pattern tree which can also be used to generate conditional patterns and suitable trees can be drawn for all the items.