To these ends, around 25 Weka blocks have been added to the e-Science Central workflow palette. The purpose of this is to extend the data mining capabilities of the e-Science Central platform using trusted, widely used software components in such a way that the non-machine learning specialist can apply these techniques to their own data easily. This paper describes the integration and use of elements of the Weka open source machine learning toolkit within the cloud based data analytics e-Science Central Platform. Weka is a mature and widely used set of Java software tools for machine learning, data-driven modelling and data mining – and is regarded as a current gold standard for the practical application of these techniques. The result shows that Decision Trees runs faster but produce high error rate and low accuracy whereas Neural Network is more preferable in Data mining although it takes longer training time, yet it yields low error rate and high accuracy. These two algorithms determine and compare the classifier errors and time taken to build the model in the explorer window and compare the Error rate of each algorithm in experimenter window. In this paper, a machine learning algorithm that combines two widely used algorithms Decision trees and Neural networks are compared and studied using Waikato Environment for Knowledge Analysis(Weka) and Iris dataset from UCI machine learning repository.
Artificial intelligence techniques are extensive and too numerous to list.
There is a range of techniques used in AI for finding hidden or unknown information in data sets (or data groups), and most of these techniques have their own sub fields. Determining the accuracy and duration of AI techniques is important for the reason that the Application of such techniques in Data mining has become wider.
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