Abstract
Mining high utility pattern has become prominent as it provides semantic significance (utility/weighted patterns) associated with items in a transaction. Data analysis and respective strategies for mining high utility patterns is important in real world scenarios. Recent researches focused on high utility pattern mining using tree-based data structure which suffers greater computation time, since they generate multiple tree branches. To cope up with these problems, this work proposes a novel binary tree-based data structure with average maximum utility (AvgMU) and mining algorithm to mine high utility patterns from incremental data which reduces tree constructions and computation time. The proposed algorithms are implemented using synthetic, real datasets and compared with state-of-the-art tree-based algorithms. Experimental results show that the proposed work has better performance in terms of running time, scalability and memory consumption than the other algorithms compared in this research work.
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