Clustering for Data Mining A Data Recovery Approach 1st Edition by Boris Mirkin ISBN 1584885343 978-1584885344

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ISBN 10: 1584885343 
ISBN 13: 978-1584885344
Author: Boris Mirkin

Clustering for Data Mining: A Data Recovery Approach (1st Edition) is a book that delves into the methods and techniques used in clustering, a fundamental aspect of data mining. The book focuses on the application of clustering algorithms and how they can be integrated with data recovery strategies to handle incomplete, noisy, or missing data. This combination of clustering and data recovery is crucial in many real-world data mining applications, as real-world datasets are often imperfect and require robust techniques for extraction and analysis.

Clustering for Data Mining: A Data Recovery Approach 1st Table of contents:

• Basic Concepts of Clustering

  • Clustering Definition and Types
  • Similarity and Dissimilarity Measures
  • Distance Metrics (e.g., Euclidean, Manhattan, Cosine Similarity)
  • Evaluation of Clustering Results

• Clustering Algorithms

  • K-Means Clustering
  • Hierarchical Clustering
  • DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
  • Gaussian Mixture Models (GMM)
  • Self-Organizing Maps (SOM)

• Data Recovery in Clustering

  • Introduction to Data Recovery Techniques
  • Handling Missing Data
  • Imputation Methods for Data Recovery
  • Clustering with Incomplete or Noisy Data

• Preprocessing for Clustering

  • Data Cleaning and Transformation
  • Feature Selection and Dimensionality Reduction
  • Scaling and Normalization of Data
  • Handling Outliers in Data

• Advanced Clustering Techniques

  • Fuzzy Clustering
  • Spectral Clustering
  • Agglomerative vs. Divisive Clustering
  • Clustering High-Dimensional Data
  • Time-Series Clustering

• Cluster Validation and Evaluation

  • Internal vs. External Validation
  • Silhouette Score and Dunn Index
  • Cross-validation Techniques in Clustering
  • Visualizing and Interpreting Clusters

• Applications of Clustering in Data Mining

  • Clustering in Market Segmentation
  • Clustering in Image and Text Mining
  • Bioinformatics and Clustering in Genomics
  • Anomaly Detection Using Clustering

• Challenges in Clustering and Data Recovery

  • Scalability of Clustering Algorithms
  • Dealing with High-Dimensional Data
  • Cluster Interpretability
  • Handling Uncertainty and Noise in Data

• Case Studies and Practical Examples

  • Case Study 1: Clustering for Customer Segmentation
  • Case Study 2: Clustering for Text Document Organization
  • Case Study 3: Clustering in Healthcare Data Analysis

• Recent Trends and Research in Clustering

  • Deep Learning Approaches to Clustering
  • Clustering in Big Data Environments
  • Hybrid Clustering Methods
  • Clustering for Cloud and Distributed Computing

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