Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
NeXt generation/dynamic spectrum access/cognitive radio wireless networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Mining periodic patterns with gap requirement from sequences
ACM Transactions on Knowledge Discovery from Data (TKDD)
Efficient Mining of Closed Repetitive Gapped Subsequences from a Sequence Database
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Mining spectrum usage data: a large-scale spectrum measurement study
Proceedings of the 15th annual international conference on Mobile computing and networking
Mining complex patterns across sequences with gap requirements
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
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Cognitive radio appears as a promising technology to allocate wireless spectrum between licensed and unlicensed users. Predictive methods for inferring the availability of spectrum holes can help to reduce collision and improve spectrum extraction. This paper introduces a Partial Periodic Pattern Mining (PPPM) algorithm to identify frequent spectrum occupancy patterns that are hidden in the spectrum usage of a channel. The mined frequent patterns are then used to predict future channel states (i.e., busy or idle). PPPM outperforms traditional Frequent Pattern Mining (FPM) by considering real patterns that do not repeat perfectly. Using real life network activities, we show a significant reduction on miss rate in channel state prediction.