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Full Description
Clustering, a foundational technique in data analytics, finds diverse applications across scientific, technical, and business domains. Within the theme of "Data Clustering," this book assumes substantial importance due to its indispensable clustering role in various contexts.
As the era of online media facilitates the rapid generation of large datasets, clustering emerges as a pivotal player in data mining and machine learning. At its core, clustering seeks to unveil heterogeneous groups within unlabeled data, representing a crucial unsupervised task in machine learning. The objective is to automatically assign labels to each unlabeled datum with minimal human intervention. Analyzing this data allows for categorization and drawing conclusions applicable across diverse application domains. The challenge with unlabeled data lies in defining a quantifiable goal to guide the model-building process, constituting the central theme of clustering.
This book presents concepts and different methodologies of data clustering. For example, deep clustering of images, semi-supervised deep clustering, deep multi-view clustering, etc. This book can be used as a reference for researchers and postgraduate students in related research background.
Contents
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.
