An Optimized Model for Breast Cancer Prediction Using Frequent Itemsets Mining

Abstract

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.

GNVDF: A GPU-accelerated Novel Algorithm for Finding Frequent Patterns Using Vertical Data Format Approach and Jagged Array

“…It is also supported by other sciences like neural networks, pattern recognition, spatial data analysis, image databases and signal processing [2,3]. There are several techniques in data mining like classification, clustering, association rule mining and regression [4]. Frequent Pattern Mining (FPM) is a computationally crucial step in data mining [5].…”