Abstract
Association rules mining on stock market in the analysis of the way toward finding substantial, valuable, and reasonable information in data. Because of the large size of databases, significance of data consumes, and profitable data acquired, finding concealed in data has turned out to be progressively noteworthy. A period arrangement dataset comprises groupings of qualities or occasions that change with time. Time arrangement data is well known in numerous applications, for example, the everyday shutting prices of an offer in a stock market. Stock data mining assumes an imperative part to picture the conduct of the financial market. Association rule mining designs can be utilized to find all thing associations (or rules) in a dataset that fulfill client determined requirements, i.e., least help and least certainty. Since just a single least help is utilized for the entire database, it is certainly accepted that all things are of a similar sort or potentially have comparative frequencies in the data. Examples are assessed by methods for producing item sets utilizing predefined support and association rules with a higher certainty level. The example created by the regular item set of size three is observed to be the same as being reflected by methods for acquired association rules. The patterns are created causes investors to assemble their portfolio and utilize these patterns to take in more about investment planning and financial market with methodology and review analysis.
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Reddy, R.V., Venkateswara Rao, K., Kameswara Rao, M., Deepak Kumar, B.P. (2023). A Review on Stock Market Analysis Using Association Rule Mining. In: Gunjan, V.K., Suganthan, P.N., Haase, J., Kumar, A. (eds) Cybernetics, Cognition and Machine Learning Applications. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-19-1484-3_20
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