FINANCIAL BANKING DATASET COMPARISION BY USING RULE- BASED CLASSIFICATION METHODS IN MACHINE LEARNING TOOL

COMPUTER SCIENCE

Data mining is the process is to extract information from a data set and transform it into an understandable structure. There are several major data mining techniques have been developing and using in data mining projects recently including classification, clustering, prediction, sequential patterns and decision tree. With the huge amount of information available online, the World Wide Web is a fertile area for data mining research. The data sets which contain marketing data can be used for two different business goals. Prediction of the results of the marketing campaign for each customer and clarification of the factors which affect the campaign results, and second are finding out customers segments, using data for the customers. In order to optimize the marketing campaigns with the help of data sets, we can use these steps, initially import data from the data sets and perform high level analysis, clean the irrelevant data and predict data by using machine learning techniques. Classification is a major technique in data mining and widely used in various fields. Four rule based classification algorithm considered are Decision Table, One R, PART and Zero R. For comparing the four algorithm three performance parameters number of correct or incorrect instances, error rate and execution time are considered. This research work also shows that which algorithm is most suitable for predicting the performance of the selected algorithms. Our work shows the process of WEKA analysis of file converts and selection of attributes to be mined and comparison with Knowledge Extraction of Evolutionary Learning not only analysis the data mining classifications but also the genetic, evolutionary algorithms is the best efficient tool in learning.

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