Abstract
High utility itemsets are having utility more than user-specified minimum utility. These itemsets provide high profit but do not exhibit correlation between them. High utility itemsets mining generate huge number of itemsets considering only single interesting criteria. Existing algorithm mines correlated high utility itemsets mining extracts itemsets that provide high utility with correlation between them. The limitation of this algorithm is that it does not consider the rarity of itemsets. To overcome this limitation, this proposed algorithm mines rare correlated high utility itemsets. Firstly, it mines correlated high utility itemsets. Secondly, it determines whether the itemsets support is no greater than minsup specified by the user. It can be shown by experimental results that the proposed algorithm reduces considerably runtime and number of candidate itemsets.
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Lalitha Kumari, P., Sanjeevi, S.G., Madhusudhana Rao, T.V. (2018). Rare Correlated High Utility Itemsets Mining: An Experimental Approach. In: Pattnaik, P., Rautaray, S., Das, H., Nayak, J. (eds) Progress in Computing, Analytics and Networking. Advances in Intelligent Systems and Computing, vol 710. Springer, Singapore. https://doi.org/10.1007/978-981-10-7871-2_73
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DOI: https://doi.org/10.1007/978-981-10-7871-2_73
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