An Efficient Association Rule Mining and Novel Genetic Algorithm for the Identification of Top–L Element

Frequent sets play an essential role in many Data Mining tasks that try to find interesting patterns from databases, such as association rules, correlations, sequences, episodes, classifiers and clusters. The mining of association rules is one of the most popular problems of all these. The identification of sets of items, products, symptoms and characteristics, which often occur together in the given database, can be seen as one of the most basic tasks in Data Mining. In this paper, a fast and efficient association rule mining algorithm has been proposed for reducing the cost and compresses the database by removing unnecessary transaction records and data items from the database that are not used for further processing. The speed of algorithm is increased because it needs to scan only the compressed database and not the entire database. Then the top l elements or most frequent item sets are identified based on the novel genetic algorithm.