Data Mining and Analysis in the Engineering Field, 1st Edition

  • Vishal Bhatnagar
  • Published By:
  • ISBN-10: 1466660872
  • ISBN-13: 9781466660878
  • DDC: 006.3
  • Grade Level Range: College Freshman - College Senior
  • 335 Pages | eBook
  • Original Copyright 2014 | Published/Released January 2016
  • This publication's content originally published in print form: 2014

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Particularly in the fields of software engineering, virtual reality, and computer science, data mining techniques play a critical role in the success of a variety of projects and endeavors. Understanding the available tools and emerging trends in this field is an important consideration for any organization. Data Mining and Analysis in the Engineering Field explores current research in data mining, including the important trends and patterns and their impact in fields such as software engineering. With a focus on modern techniques as well as past experiences, this vital reference work will be of greatest use to engineers, researchers, and practitioners in scientific-, engineering-, and business-related fields.

Table of Contents

Front Cover.
Title Page.
Copyright Page.
Advances in Data Mining and Database Management (ADMDM) Book Series.
Titles in this Series.
Editorial Advisory Board.
List of Reviewers.
Table of Contents.
Detailed Table of Contents.
1: Optimal Features for Metamorphic Malware Detection.
2: Application of Data Mining and Analysis Techniques for Renewable Energy Network Design and Optimization.
3: Visualizing the Bug Distribution Information Available in Software Bug Repositories.
4: Applications of Data Mining in Software Development Life Cycle: A Literature Survey and Classification.
5: Determination of Pull Out Capacity of Small Ground Anchor Using Data Mining Techniques.
6: Rules Extraction using Data Mining in Historical Data.
7: Robust Statistical Methods for Rapid Data Labelling.
8: Mathematical Statistical Examinations on Script Relics.
9: Rough Set on Two Universal Sets Based on Multigranulation.
10: Rule Optimization of Web-Logs Data Using Evolutionary Technique.
11: Machine Learning Approaches for Sentiment Analysis.
12: Combining Semantics and Social Knowledge for News Article Summarization.
13: A Layered Parameterized Framework for Intelligent Information Retrieval in Dynamic Social Network using Data Mining.
14: Implementation of Mining Techniques to Enhance Discovery in Service–Oriented Computing.
15: Overview of Business Intelligence through Data Mining.
16: Population-Based Feature Selection for Biomedical Data Classification.
17: A Comparative Study on Medical Diagnosis Using Predictive Data Mining: A Case Study.
Compilation of References.
About the Contributors.