Improved Apriori Algorithm using logarithmic decoding and pruning

In computer science and data mining, Apriori is a classic algorithm for learning association rules. Apriori is designed to operate on databases containing transactions. The purpose of the Apriori Algorithm is to find associations between different sets of data. It is sometimes referred to as "Market Basket Analysis". The aim of this research is to improve the performance of the conventional Apriori algorithm that mines the association rules. The approach is to attain the desired improvement is to create a more efficient new algorithm out of the conventional one by adding the encoding and decoding mechanisms to the latter in order to demonstrate the importance of the efficient decoding to high data mining performance and from various experiments it is proved that the logarithmic decoding method is the most efficient among the all methods it can speed up all the required processes.