Abstract
Frequent itemset mining is playing an important research role for many aspirants all over the world. In all the research aspects efficiency and scalability are main retrospects which are being improved in FIM which is intensive. In the current scenario, the implementation of a sequential growth algorithm on the big data map reduce framework the lexicographic sequential tree construction for the identification of the frequent itemsets using the lexicographical order over the databases of transaction without incorporating any extreme search methodologies. The result signifies a wide variety of large database executions to prove the execution of this methodology as an efficient and improved scalability of the methodology. Further the incorporation of this technique with other pattern mining is quite beneficial on big data.
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Venkateswara Rao, P., Srinivasa Rao, D., Sucharita, V. (2020). Frequent Item-Set Mining Using Lexicographical Sequential Tree Construction on Map Reduce Framework. In: Jyothi, S., Mamatha, D., Satapathy, S., Raju, K., Favorskaya, M. (eds) Advances in Computational and Bio-Engineering. CBE 2019. Learning and Analytics in Intelligent Systems, vol 15. Springer, Cham. https://doi.org/10.1007/978-3-030-46939-9_12
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