DESIGN AND DEVELOPMENT OF ROBUST ALGORITHM USING ID3,CHAID AND NAIVE BAYES FOR INFORMATION EXTRACTION

Asha Gill

ISBN 10: 1723921270 / ISBN 13: 9781723921278

Published by Independently published, 2018

About this Item

67 pages. 10.00x8.00x0.17 inches. In Stock.

Seller Inventory # 1723921270

Bibliographic Details

• Title: DESIGN AND DEVELOPMENT OF ROBUST ALGORITHM USING ID3,CHAID AND NAIVE BAYES FOR INFORMATION EXTRACTION
• Publisher: Independently published
• Publication Date: 2018
• Language: English
• ISBN 10: 1723921270
• ISBN 13: 9781723921278
• Binding: Paperback
• Condition: Brand New

About this title

Synopsis

In the epic world, storage and accessing methods for information were limited. For accessing the data rock drawings, papers etc. were used. So, getting accurate information was quite difficult at that period of time. Management of data waseasyat that time but with the advancement in technology challenges had been also increasing. Today a huge amount of data is produced within each and every passing minute. Data initially treated as a collection of raw facts but after processing of data information is extracted. Here data mining plays an important role for the extraction of only relevant and useful data for the users. Data mining algorithms have been widely used in the area of decision making, Artificial intelligence, Neural networks etc. In this an introduction on what data is and how it has been transformed into useful knowledge has been discussed. Along with it essence of data mining and how a rapid change occurs in the storage and accessing of data have been discussed. A comparative analysis of different techniques used for data mining with their advantages and limitations has been discussed. Some of the important tools that have been used for extraction of information also presented.Three major algorithms of classification ID3, CHAID and Naïve Bayes have been implemented in Rapid Miner on four different data sets. An algorithm has been proposed for the implementation of these algorithms on random data sets. From the empirical results ID3 classifies more accurately with an accuracy rate of 98.04%. A performance optimizer has been build using PHP language that includes all the data sets and algorithm details. This performance optimizer suggests the users that which classifier is classifying more accurately. Based on three parameters accuracy, precision and recall ID3 founds as more accurate in all three cases.