Frequent pattern mining is a heavily researched area in the field of data mining with wide range of applications.Mining frequent patterns from large scale databases has emerged as an important problem in data mining .A number of algorithms has been proposed to determine frequent patterns. Apriori algorithm is the first algorithmproposed in this field. With the time a number of changes proposed in Apriori to enhance the performance in term of time and number of database passes. This paper proposes an innovative utility sentient approach for the mining of interesting association patterns from transaction database and also illustrate the limitations of apriorialgorithm . First, frequent patterns are discovered from the transactional database using the apriori algorithm. From the frequent patterns mined, this approach extracts novel interesting association patterns with emphasis on significance, quantity , profit and confidence. A comparative analysis is also presented to illustrate ourapproach’s effectiveness
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