Data Mining Algorithms for Disaster Data Analysis

S. Vijayarani Mohan

ISBN 10: 3659489042 / ISBN 13: 9783659489044

Published by LAP LAMBERT Academic Publishing, 2015

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - Nowadays, data analysis becomes more important and essential to get accurate knowledge and making good decisions. Data mining research helps to carry out indepth data analysis for different kinds of data, from which, we can infer the hidden knowledge. 'Data Mining Algorithms for Disaster Data Analysis' performed a detailed analysis on disaster data set using classification algorithms. The classification algorithms used in this analysis are IBK, KStar, LWL, J48, Random Forest, Random Tree, Naïve Bayes, Tree Decision Stump, Single Rule Induction, CHAID, QUEST, Exhaustive CHAID and CRT. This comparative analysis is implemented in three different data mining tools, WEKA, Rapidminer and SPSS. This book studied the data mining basics and also described how to perform classification tasks using these tools. This information will be useful for the students, researchers and professionals to pursue their work in the area of data mining.

Seller Inventory # 9783659489044

Bibliographic Details

• Title: Data Mining Algorithms for Disaster Data Analysis
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2015
• Language: English
• ISBN 10: 3659489042
• ISBN 13: 9783659489044
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 8D millimeters 220 millimeters width by 150 millimeters height by 8 millimeters depth
• Weight: 209 grams

About this title

Synopsis

Nowadays, data analysis becomes more important and essential to get accurate knowledge and making good decisions. Data mining research helps to carry out indepth data analysis for different kinds of data, from which, we can infer the hidden knowledge. “Data Mining Algorithms for Disaster Data Analysis” performed a detailed analysis on disaster data set using classification algorithms. The classification algorithms used in this analysis are IBK, KStar, LWL, J48, Random Forest, Random Tree, Naïve Bayes, Tree Decision Stump, Single Rule Induction, CHAID, QUEST, Exhaustive CHAID and CRT. This comparative analysis is implemented in three different data mining tools, WEKA, Rapidminer and SPSS. This book studied the data mining basics and also described how to perform classification tasks using these tools. This information will be useful for the students, researchers and professionals to pursue their work in the area of data mining.