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A Survey on High Utility Data Mining for Increasing Transactions Databases
Abstract
High utility itemset mining is significantly increased and became popular because of the real-time transactions. Current high utility itemset mining techniques are focused on different individual constraint which are its own performance. But in data mining, discovering new knowledge from the large database, it is important to follow the certain constraints (mixed) to accomplish the task effectively and profitably. All the existing literature we found that the algorithms are designed and developed in static databases based on single constraints. But in case of real-time applications, certain operations like insert or delete or updating are being done for every minute. So, this paper, study about different algorithms and challenges of algorithms on high utility data mining including incremental high utility mining and periodical also. This paper also studies and propose usage of multiple constraints on high utility data mining and measure about the accuracy of the algorithms using different constraints on data, model and measures.
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References
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