You are previewing International Journal of Rough Sets and Data Analysis (IJRSDA) Volume 3, Issue 2.
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International Journal of Rough Sets and Data Analysis (IJRSDA) Volume 3, Issue 2

Book Description

The International Journal of Rough Sets and Data Analysis (IJRSDA) is a multidisciplinary journal that publishes high-quality and significant research in all fields of rough sets, granular computing, and data mining techniques. Rough set theory is a mathematical approach concerned with the analysis and modeling of classification and decision problems involving vague, imprecise, uncertain, or incomplete information. Rough sets have been proposed for a variety of applications, including artificial intelligence and cognitive sciences, especially machine learning, knowledge discovery, data mining, expert systems, approximate reasoning, and pattern recognition. The journal extends existing research findings (theoretical innovations and modeling applications) to provide the highest quality original concepts, hybrid applications, innovative methodologies, and development trends studies for all audiences. This journal publishes original articles, reviews, technical reports, patent alerts, and case studies on the latest innovative findings of new methodologies and techniques.

This issue contains the following articles:

  • Improving Efficiency of K-Means Algorithm for Large Datasets
  • A Study of Sub-Pattern Approach in 2D Shape Recognition Using the PCA and Ridgelet PCA
  • EEG Analysis of Imagined Speech
  • Analysis of Gait Flow Image and Gait Gaussian Image Using Extension Neural Network for Gait Recognition
  • Hybrid Data Mining Approach for Image Segmentation Based Classification
  • Movie Analytics for Effective Recommendation System using Pig with Hadoop

Table of Contents

  1. Cover
  2. Masthead
  3. Call For Articles
  4. Improving Efficiency of K-Means Algorithm for Large Datasets
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. RELATED WORK
    4. 3. PROPOSED METHODOLOGY
    5. 4. EXPERIMENTATION AND RESULTS
    6. 5. CONCLUSION AND FUTURE WORK
    7. REFERENCES
  5. A Study of Sub-Pattern Approach in 2D Shape Recognition Using the PCA and Ridgelet PCA
    1. ABSTRACT
    2. INTRODUCTION
    3. RELATED WORK
    4. PRELIMINARIES
    5. RIDGETLET TRANSFORM
    6. PRINCIPAL COMPONENT ANALYSIS
    7. DISTANCE MEASURE CLASSIFICATION TECHNIQUES
    8. PROPOSED METHOD
    9. EXPERIMENTAL RESULTS AND COMPARATIVE STUDY
    10. EXPERIMENTAL ANALYSIS
    11. CONCLUSION
    12. REFERENCES
  6. EEG Analysis of Imagined Speech
    1. ABSTRACT
    2. INTRODUCTION
    3. METHODOLOGY
    4. DATA PREPROCESSING
    5. FEATURE EXTRACTION
    6. CLASSIFICATION
    7. RESULTS
    8. DISCUSSION
    9. CONCLUSION
    10. REFERENCES
  7. Analysis of Gait Flow Image and Gait Gaussian Image Using Extension Neural Network for Gait Recognition
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. LITREATURE SURVEY
    4. 3. IMPLEMENTATION METHOD
    5. 4. EXPERIMENTAL RESULTS
    6. 5. CONCLUSION
    7. REFERENCES
  8. Hybrid Data Mining Approach for Image Segmentation Based Classification
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. RELATED WORK
    4. 3. PROPOSED METHODOLOGY
    5. 4. IMPLEMENTATION
    6. 5. RESULTS AND DISCUSSION
    7. 6. CONCLUSION
    8. REFERENCES
  9. Movie Analytics for Effective Recommendation System using Pig with Hadoop
    1. ABSTRACT
    2. INTRODUCTION
    3. RELATED RESEARCH WORK AND MOTIVATION
    4. RESEARCH FRAMEWORK
    5. DATA GATHERING
    6. EXPERIMENTAL FINDINGS
    7. ADVANTAGES OF PIG LATIN OVER STRUCTURED QUERY LANGUAGE (SQL)
    8. LIMITATIONS
    9. CONCLUSION AND FUTURE WORK
    10. ACKNOWLEDGMENT
    11. REFERENCES
  10. Call For Articles