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Real World Data Mining Applications

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Cover of 'Real World Data Mining Applications'

Table of Contents

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    Book Overview
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    Chapter 1 Introduction
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    Chapter 2 What Data Scientists Can Learn from History
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    Chapter 3 On Line Mining of Cyclic Association Rules From Parallel Dimension Hierarchies
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    Chapter 4 PROFIT: A Projected Clustering Technique
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    Chapter 5 Multi-label Classification with a Constrained Minimum Cut Model
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    Chapter 6 On the Selection of Dimension Reduction Techniques for Scientific Applications
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    Chapter 7 Relearning Process for SPRT in Structural Change Detection of Time-Series Data
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    Chapter 8 K -means Clustering on a Classifier-Induced Representation Space: Application to Customer Contact Personalization
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    Chapter 9 Dimensionality Reduction Using Graph Weighted Subspace Learning for Bankruptcy Prediction
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    Chapter 10 Click Fraud Detection: Adversarial Pattern Recognition over 5 Years at Microsoft
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    Chapter 11 A Novel Approach for Analysis of ‘Real World’ Data: A Data Mining Engine for Identification of Multi-author Student Document Submission
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    Chapter 12 Data Mining Based Tax Audit Selection: A Case Study of a Pilot Project at the Minnesota Department of Revenue
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    Chapter 13 A Nearest Neighbor Approach to Build a Readable Risk Score for Breast Cancer
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    Chapter 14 Machine Learning for Medical Examination Report Processing
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    Chapter 15 Data Mining Vortex Cores Concurrent with Computational Fluid Dynamics Simulations
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    Chapter 16 Real World Data Mining Applications
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    Chapter 17 Exploiting Terrain Information for Enhancing Fuel Economy of Cruising Vehicles by Supervised Training of Recurrent Neural Optimizers
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    Chapter 18 Exploration of Flight State and Control System Parameters for Prediction of Helicopter Loads via Gamma Test and Machine Learning Techniques
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    Chapter 19 Multilayer Semantic Analysis in Image Databases
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Title
Real World Data Mining Applications
Published by
Springer International Publishing, November 2014
DOI 10.1007/978-3-319-07812-0
ISBNs
978-3-31-907811-3, 978-3-31-907812-0
Editors

Abou-Nasr, Mahmoud, Lessmann, Stefan, Stahlbock, Robert, Weiss, Gary M.

Mendeley readers

The data shown below were compiled from readership statistics for 36 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Canada 1 3%
Unknown 35 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 6%
Student > Master 2 6%
Student > Doctoral Student 1 3%
Student > Bachelor 1 3%
Researcher 1 3%
Other 0 0%
Unknown 29 81%
Readers by discipline Count As %
Computer Science 3 8%
Mathematics 1 3%
Engineering 1 3%
Unknown 31 86%