Abstract
Association rule analysis, as one of the significant means of data mining, plays an important role in discovering the implicit knowledge in massive transaction data. Aiming at the inherent defects of the classic Apriori algorithm, this paper proposes IAPS (Improved Apriori with Prejudging and Screening) algorithm. IAPS algorithm adds a prejudging and screening procedure on the basis of the self-join and pruning progress in Apriori algorithm which can reduce and optimize the k-frequent item sets using prior probability. IAPS algorithm simplifies the operation process of mining frequent item sets. Experimental results show that the improved algorithm can effectively reduce the number of scanning databases and reduce the running time of the algorithm.
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Acknowledgements
 Project supported by the National Natural Science Foundation of China under Grant nos. 61373135, 61300240, 61401225, 61502252; the Natural Science Foundation of Jiangsu Province of China under Grant nos. BK20140883, BK20140894, BK20131377; China Postdoctoral Science Foundation funded project under Grant no. 2015M581844; Jiangsu Planned Projects for Postdoctoral Research Funds under Grant no. 1501125B; NUPTSF under Grant no. NY214101, NY215147.
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Zhao, X., Li, D., Yuan, Y., Sun, Z., Chen, Y. (2017). An Improved Apriori Algorithm with Prejudging and Screening. In: Bhatia, S., Mishra, K., Tiwari, S., Singh, V. (eds) Advances in Computer and Computational Sciences. Advances in Intelligent Systems and Computing, vol 553. Springer, Singapore. https://doi.org/10.1007/978-981-10-3770-2_61
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DOI: https://doi.org/10.1007/978-981-10-3770-2_61
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