Optimization of Active Apriori Algorithm Using an Effective Genetic Algorithm for the Identification of Top-l Elements in a Peer to Peer Network
Keywords:Data mining, Active Apriori algorithm, Effective Genetic algorithm.
In a distributed system like peer to peer network, there are two ways of storing the data namely homogeneous and heterogeneous. Mining the homogeneous dataÂ in a client is less time consuming and fast compared to the mining in the server. 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 Â popular problems of all these. In this paper, Active Apriori algorithm is used to find the frequent items in the data set which reduces the cost. This method 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 entire database. The results of the Active apriori algorithm can be optimized using an effective genetic algorithm to identify the top l elements or most frequent item sets. In this method,Â the near distance of rule set are found using equalize distance formula and generate two classes namely, higher class and lower class . The classes are validated by distance weight vector, whichÂ maintains a threshold value of rule item set. This Effective genetic algorithm is mainly used for optimization of rule set.
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