Abstract
In this paper, we propose a method for discovering hidden information from large-scale item set data based on the symmetry of items. Symmetry is a fundamental concept in the theory of Boolean functions, and there have been developed fast symmetry checking methods based on BDDs (Binary Decision Diagrams). Here we discuss the property of symmetric items in data mining problems, and describe an efficient algorithm based on ZBDDs (Zero-suppressed BDDs). The experimental results show that our ZBDD-based symmetry checking method is efficiently applicable to the practical size of benchmark databases.
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Minato, Si. (2006). Symmetric Item Set Mining Based on Zero-Suppressed BDDs. In: Todorovski, L., Lavrač, N., Jantke, K.P. (eds) Discovery Science. DS 2006. Lecture Notes in Computer Science(), vol 4265. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893318_35
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DOI: https://doi.org/10.1007/11893318_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46491-4
Online ISBN: 978-3-540-46493-8
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