Abstract
Data mining seeks to discover hidden relationship in data attributes for decision making. Mostly, search algorithms help to find hidden relationships between data attributes by counting the number of occurrence without focusing on the closeness of time dimension. In this chapter, we focus on how closeness preference model can be applied in discovering association rules instead of only using support and confidence value which are the traditional method of discovering association rules.
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Millham, R., Agbehadji, I.E., Yang, H. (2021). Extracting Association Rules: Meta-Heuristic and Closeness Preference Approach. In: Fong, S., Millham, R. (eds) Bio-inspired Algorithms for Data Streaming and Visualization, Big Data Management, and Fog Computing. Springer Tracts in Nature-Inspired Computing. Springer, Singapore. https://doi.org/10.1007/978-981-15-6695-0_5
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