DATA MINING METHODOLOGY DEVELOPMENT: CLASSIFICATION CASE STUDY ON MARS CRATERS DETECTION AND VISUALIZATION ANALYSIS USING SAMMON*S MAPPING

Wang, Jue

ISBN 10: 3843364737 / ISBN 13: 9783843364737

Published by LAP LAMBERT Academic Publishing, 2010

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Seller Inventory # ERICA82938433647376

Bibliographic Details

• Title: DATA MINING METHODOLOGY DEVELOPMENT: CLASSIFICATION CASE STUDY ON MARS CRATERS DETECTION AND VISUALIZATION ANALYSIS USING SAMMON*S MAPPING
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2010
• Language: English
• ISBN 10: 3843364737
• ISBN 13: 9783843364737
• Binding: paperback
• Condition: New
• Book Type: book

About this title

Synopsis

Data Mining is a tool that extracts useful and novel patterns from great amounts of data. It usually involves 4 tasks: Classification, Clustering, Association Rule Learning, and Regression. Two major branches of research are addressed in this book. In the first chapter several Classification methods are applied on a real-world data set, the Mars crater data set. The goal of this case study is to improve the accuracy of the crater detection on the remote sensing images of Mars. In the second chapter Sammon's Mapping method is studied and improved. Sammon's Mapping is a projection method which simulates the high-dimension space to a low- dimension one. The motivation of this project is to visualize the internal struture of a data set and facilitate the clustering operation on the data set. After the low-dimension Sammon's Mapped space has been created the number of clusters can be observed. An external measurement of the Clustering result is also implemented in the project. This measurement objectively shows the accuracy of Clustering. With all the steps to implement the Sammon's Mapping, a pipeline is established.

About the Author

Jue Wang was born in Shanghai, China. She received the M.S. Degree in Computer Science from University of Massachusetts Boston, USA. During the time in the graduate school, she organized an association, Women in Science, to encourage the female students in science domains. Her research interests include data mining and machine learning.