Abstract
With the expansion and future potential in the field of healthcare industry, it is necessary to analyze a large amount of noisy data to obtain meaningful knowledge. Data mining techniques can be applied to remove inconsistent data and extract significant patterns. Association rule mining is a rule-based method which uncovers how items are associated with each other. Apriori algorithm is a broadly used algorithm for mining frequent itemsets for association rule mining. However, the performance of the apriori algorithm degrades with the large volume of data. So a parallel and distributed algorithm is required for efficient mining. In this paper, we provide an efficient implementation of the apriori algorithm in Hadoop MapReduce framework. We have considered medical data to produce rules which could be used to find the association between disease and their symptoms. These rules can be used for knowledge discovery to provide guidelines for the healthcare industry.
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Bhattacharya, N., Mondal, S., Khatua, S. (2019). A MapReduce-Based Association Rule Mining Using Hadoop Cluster—An Application of Disease Analysis. In: Saini, H., Sayal, R., Govardhan, A., Buyya, R. (eds) Innovations in Computer Science and Engineering. Lecture Notes in Networks and Systems, vol 74. Springer, Singapore. https://doi.org/10.1007/978-981-13-7082-3_61
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DOI: https://doi.org/10.1007/978-981-13-7082-3_61
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