文章基本信息

标题：An Optimized Model for Breast Cancer Prediction Using Frequent Itemsets Mining
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作者：Ankita Sinha ; Bhaswati Sahoo ; Siddharth Swarup Rautaray 等
期刊名称：International Journal of Information Engineering and Electronic Business
印刷版ISSN：2074-9023
电子版ISSN：2074-9031
出版年度：2019
卷号：11
期号：5
页码：11-18
DOI：10.5815/ijieeb.2019.05.02
出版社：MECS Publisher
摘要：This presented research paper mainly studies the frequent itemsets mining approach for finding the most important attribute to overcome the existing problems in the extraction of relevant information by using data mining approaches from a huge amount of dataset. Firstly a state of art diagram for prediction is designed and data mining classifier like naive bayes, support vector machine, decision tree, k- nearest neighbour are compared and then proposed methodology with new techniques are proposed. Moreover, a new attribute filtering association frequent itemsets mining algorithm is presented. Then, by analyzing the feasibility of the proposed algorithm, the data mining classification classifier is compared. As a result, SVM produces the best result among all the classifier with attribute filtrating and without attribute filtrating. With attribute filtrating algorithm enhances the accuracy of all the other classifier.
关键词：Association rule mining;Frequent itemsets mining;Decision tree;Naive bayes;Support vector machine;k-nearest neighbour;Prediction