Abstract
High utility patterns mining from transaction databases is an important research area in the field of data mining. Due to the unavailability of downward closure property among the utilities of the itemsets it becomes great challenge to the researchers. Even though, efficient pruning strategy called, transaction weighted utility downward closure property used to reduce the number of candidate itemsets, total time to generate and test candidate itemsets is more. In view of this, in this paper we have proposed a time-efficient tree-based algorithm (TTBM) for mining high utility patterns from transaction databases. We construct conditional pattern bases to generate high transaction weighted utility patterns in the second pass of our algorithm. We used an efficient tree structure called, HP-Tree and tracing method to keep high transaction weighted utility patterns and for discovering high utility patterns respectively. We have compared the performance against Two-Phase and HUI-Miner algorithms. The experimental results show that the execution time of our approach is better.
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Manike, C., Om, H. (2015). Time-Efficient Tree-Based Algorithm for Mining High Utility Patterns. In: El-Alfy, ES., Thampi, S., Takagi, H., Piramuthu, S., Hanne, T. (eds) Advances in Intelligent Informatics. Advances in Intelligent Systems and Computing, vol 320. Springer, Cham. https://doi.org/10.1007/978-3-319-11218-3_37
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DOI: https://doi.org/10.1007/978-3-319-11218-3_37
Publisher Name: Springer, Cham
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