Abstract
Pattern mining has been more important in the solution of various data mining jobs over the years. The extraction of common patterns was the primary focus of pattern mining research for a long period of time, with the mining of rare patterns being neglected. Rare pattern mining is becoming more popular as researchers recognize the importance of rare patterns. The hyper-linked data structure is suitable to store sparse data set in the main memory and enables dynamic adjustment of links during the mining process using recursion. However, a sequential approach to discovering rare patterns from a large dataset is inefficient. Hence a CUDA-based parallel algorithm has been implemented to discover rare itemsets. The algorithm is tested using dense and sparse datasets on a GPU. The GPU initialization time affects the time taken to discover rare itemsets. The time taken to transfer data between CPU and GPU is significantly large and the parallel implementation of an algorithm with a recursive approach is unsuitable.
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Yadavalli, G., Rai, S. (2023). Discovery of Rare Itemsets Using Hyper-Linked Data Structure: A Parallel Approach. In: Prabhu, S., Pokhrel, S.R., Li, G. (eds) Applications and Techniques in Information Security . ATIS 2022. Communications in Computer and Information Science, vol 1804. Springer, Singapore. https://doi.org/10.1007/978-981-99-2264-2_23
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DOI: https://doi.org/10.1007/978-981-99-2264-2_23
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