Description

Book Synopsis

 

Chapter 1 Classification of Gougerot-Sjögren syndrome Based on Artificial Intelligence.- Chapter 2 Deep learning Classification of Venous Thromboembolism based on Ultrasound imaging.- Chapter 3 Synchronization-Driven Community Detection: Dynamic Frequency Tuning Approach.- Chapter 4 Automatic Evolutionary Clustering for Human Activity Discovery.- Chapter 5 Identification of Correlated factors for Absenteeism of employees using Clustering techniques.- Chapter 6 Multi-view Data Clustering through Consensus Graph and Data Representation Learning.- Chapter 7 Uber's Contribution to Faster Deep Learning: A Case Study in Distributed Model Training.- Chapter 8 Auto-Weighted Multi-View Clustering with Unified Binary Representation and Deep Initialization.- Chapter 9 Clustering with Adaptive Unsupervised Graph Convolution Network.- Chapter 10 Graph-based Semi-supervised Learning for Multi-view Data Analysis.- Chapter 11 Advancements in Fuzzy Clustering Algorithms for Im-age Processing: A Comprehensive Review and Future Directions.- Chapter 12 Multiview Latent representation learning with feature diversity for clustering.

Advances in Data Clustering

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    Order before 4pm today for delivery by Tue 16 Jun 2026.

    A Hardback by Fadi Dornaika

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      View other formats and editions of Advances in Data Clustering by Fadi Dornaika

      Publisher: Springer
      Publication Date: 30/12/2024
      ISBN13: 9789819776788, 978-9819776788
      ISBN10:
      Also in:
      Data mining

      Description

      Book Synopsis

       

      Chapter 1 Classification of Gougerot-Sjögren syndrome Based on Artificial Intelligence.- Chapter 2 Deep learning Classification of Venous Thromboembolism based on Ultrasound imaging.- Chapter 3 Synchronization-Driven Community Detection: Dynamic Frequency Tuning Approach.- Chapter 4 Automatic Evolutionary Clustering for Human Activity Discovery.- Chapter 5 Identification of Correlated factors for Absenteeism of employees using Clustering techniques.- Chapter 6 Multi-view Data Clustering through Consensus Graph and Data Representation Learning.- Chapter 7 Uber's Contribution to Faster Deep Learning: A Case Study in Distributed Model Training.- Chapter 8 Auto-Weighted Multi-View Clustering with Unified Binary Representation and Deep Initialization.- Chapter 9 Clustering with Adaptive Unsupervised Graph Convolution Network.- Chapter 10 Graph-based Semi-supervised Learning for Multi-view Data Analysis.- Chapter 11 Advancements in Fuzzy Clustering Algorithms for Im-age Processing: A Comprehensive Review and Future Directions.- Chapter 12 Multiview Latent representation learning with feature diversity for clustering.

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