Neural Networks and Rules-based Systems used to Find Rational and Scientific Correlations between being Here and Now with Afterlife Conditions
February 17, 2026
Neural Networks and Rules-based Systems used to Find Rational and
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Association rules present one of the most impressive techniques for the analysis of attribute associations in a given dataset related to applications related to retail, bioinformatics, and sociology. In the area of data mining, the importance of the rule management in associating rule mining is rapidly growing. Usually, If datasets are large, the induced rules are large in volume. The density of the rule volume leads to the obtained knowledge hard to be understood and analyze. One better way of minimizing the rule set size is eliminating redundant rules from rule base. Many efforts have been made and various competent and excellent algorithms have been proposed. But all of these models relying either on closed itemset mining or expert’s evaluation. None of these models are proven best in all data set contexts. Closed itemset model is missing adaptability and expert’s evaluation process is resulting different significance for same rule under different expert’s view. To overcome these limits here we proposed a post mining process called ARA as an extension to our earlier proposed closed itemset mining algorithm called PEPP.
Mr. Kalli Srinivasa Nageswara Prasad. 2011. \u201cAttribute Relational Analysis (ARA) for Coherent Association Rules: A post mining process for Parallel Edge Projection and Pruning (PEPP) Based Sequence Graph protrude approach for Closed Itemset\u201d. Global Journal of Research in Engineering - I: Numerical Methods GJRE-I Volume 11 (GJRE Volume 11 Issue I7): .
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Crossref Journal DOI 10.17406/gjre
Print ISSN 0975-5861
e-ISSN 2249-4596
The methods for personal identification and authentication are no exception.
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Neural Networks and Rules-based Systems used to Find Rational and
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Association rules present one of the most impressive techniques for the analysis of attribute associations in a given dataset related to applications related to retail, bioinformatics, and sociology. In the area of data mining, the importance of the rule management in associating rule mining is rapidly growing. Usually, If datasets are large, the induced rules are large in volume. The density of the rule volume leads to the obtained knowledge hard to be understood and analyze. One better way of minimizing the rule set size is eliminating redundant rules from rule base. Many efforts have been made and various competent and excellent algorithms have been proposed. But all of these models relying either on closed itemset mining or expert’s evaluation. None of these models are proven best in all data set contexts. Closed itemset model is missing adaptability and expert’s evaluation process is resulting different significance for same rule under different expert’s view. To overcome these limits here we proposed a post mining process called ARA as an extension to our earlier proposed closed itemset mining algorithm called PEPP.
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