Abstract
Extracting meaningful patterns from databases has become a significant field of research for the data mining community. Researchers have skillfully taken up this task, contributing a range of frequent and rare pattern mining techniques. Literature subdivides the pattern mining techniques into two broad categories of level-wise and tree-based approaches. Studies illustrate that tree-based approaches outshine in terms of performance over the former ones at many instances. This paper aims to provide an empirical analysis of two well-known tree-based approaches in the field of frequent and rare pattern mining. Through this paper, an attempt has been made to let the researchers analyze the factors affecting the performance of the most widely accepted category of pattern mining techniques: the tree-based approaches.
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Borah, A., Nath, B. (2019). Performance Analysis of Tree-Based Approaches for Pattern Mining. In: Behera, H., Nayak, J., Naik, B., Abraham, A. (eds) Computational Intelligence in Data Mining. Advances in Intelligent Systems and Computing, vol 711. Springer, Singapore. https://doi.org/10.1007/978-981-10-8055-5_39
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DOI: https://doi.org/10.1007/978-981-10-8055-5_39
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