Analysis and Comparison Study of Data Mining Algorithms Using Rapid Miner

Abstract

Comparison study of algorithms is very much required before implementing them for the needs of any organization. The comparisons of algorithms are depending on the various parameters such as data frequency, types of data and relationship among the attributes in a given data set. There are number of learning and classifications algorithms are used to analyse, learn patterns and categorize data are available. But the problem is the one to find the best algorithm according to the problem and desired output. The desired result has always been higher accuracy in predicting future values or events from the given dataset. Algorithms taken for the comparisons study are Neural net, SVM, Naïve Bayes, BFT and Decision stump. These top algorithms are most influential data mining algorithms in the research community. These algorithms have been considered and mostly used in the field of knowledge discovery and data mining.