Abstract
Market basket analysis techniques are useful for extracting customer’s purchase behaviors or rules by discovering what items they buy together using the association rules and correlation. Associated and correlated items are placed in the neighboring shelf to raise their purchasing probability in a super shop. Therefore, the mining combined association rules with correlation can discover frequently correlated, associated, associated-correlated and independent patterns synchronously, that are extraordinarily useful for making everyday’s business decisions. Since, the existing algorithms for mining correlated patterns did not consider the overhead of ‘null transactions’ during the mining operations; these algorithms fail to provide faster retrieval of useful patterns and besides, memory usages also increase exponentially. In this paper, we proposed an efficient approach for mining above mentioned four kinds of patterns by removing so called ‘null transactions’; by which not only possible to save precious computation time but also speeds up the overall mining process. Comprehensive experimental results show that the technique developed in this paper are feasible for mining large transactional databases in terms of time, memory usages, and scalability.
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Karim, M.R., Halder, S., Jeong, BS., Choi, HJ. (2012). Efficient Mining Frequently Correlated, Associated-Correlated and Independent Patterns Synchronously by Removing Null Transactions. In: Park, J., Jin, Q., Sang-soo Yeo, M., Hu, B. (eds) Human Centric Technology and Service in Smart Space. Lecture Notes in Electrical Engineering, vol 182. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-5086-9_13
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DOI: https://doi.org/10.1007/978-94-007-5086-9_13
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-007-5085-2
Online ISBN: 978-94-007-5086-9
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