Yazar "Akbas, Kubra Elif" seçeneğine göre listele

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    Assessment of Association Rules based on Certainty Factor: an Application on Heart Data Set
    (Ieee, 2019) Akbas, Kubra Elif; Kivrak, Mehmet; Arslan, A. Kadir; Colak, Cemil
    Association rules mining is one of the uttermost applied techniques in data mining and artificial intelligence. Support and confidence are two basic measures employed in the evaluation of association rules. The rules obtained with these two values are often correct; however, they are not strong rules. Most of the rules, especially with a high support value, are misleading. For this reason, there are many interestingness measures proposed to achieve stronger rules. In this study it is aimed to establish strong association rules with variables in open sourced heart data set. In the current study, Apriori algorithm was used to obtain the rules. As a result of the analysis, only 55 confidence and support criteria were taken into consideration. For more powerful rules, certainty factor was used as one of the interestingness measure proposed in the literature, and it was concluded that only 26 of these rules were strong. As a result of the analysis of the findings obtained in the context of the research, it can be inferred that stronger rules can be obtained by using the certainty factor in association rules mining.
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    ON ?- STATISTICAL CONVERGENCE OF ORDER a IN PROBABILITY
    (Univ Prishtines, 2017) Isik, Mahmut; Akbas, Kubra Elif
    In this study we introduce and examine the concepts of lambda-statistical convergence of order a in probability and strong (V, lambda) summability of order a in probability.
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    On Asymptotically Lacunary Statistical Equivalent Sequences of Order ? in Probability
    (E D P Sciences, 2017) Isik, Mahmut; Akbas, Kubra Elif
    In this study, we introduce and examine the concepts of asymptotically lacunary statistical equivalent of order alpha in probability and strong asymptotically lacunary equivalent of order alpha in probability. We give some relations connected to these concepts.