The book "Data Clustering: Algorithms and Applications" edited by Charu C. Aggarwal and Chandan K. Reddy provides a comprehensive overview of the field of data clustering, covering both core methods and specific applications. The book is structured into three main sections: Method Chapters, Domain Chapters, and Variations and Insights. 1. **Method Chapters**: - **Feature Selection Methods**: Discusses techniques for selecting relevant features to enhance clustering quality. - **Probabilistic and Generative Models**: Explains how to model data using probabilistic models and generative processes, often solved with the EM algorithm. - **Distance-Based Algorithms**: Introduces methods that use distance functions to partition data, including flat and divisive approaches. - **Density-Based Clustering**: Focuses on algorithms that identify clusters based on density, such as DBSCAN. - **Grid-Based Clustering**: Describes methods that use grid structures to cluster data, like STING and CLIQUE. - **Spectral Clustering**: Explains how to use spectral methods to find clusters in data represented by a similarity graph. - **Nonnegative Matrix Factorizations (NMF)**: Discusses NMF for clustering, including its theoretical foundations and practical applications. - **Clustering High-Dimensional Data**: Addressing challenges and techniques for clustering high-dimensional data. - **Clustering Stream Data**: Introduces methods for streaming data, such as k-Means and streaming algorithms. - **Big Data Clustering**: Covers techniques for handling large datasets, including one-pass algorithms and parallel methods. - **Clustering Categorical Data**: Discusses methods for categorical data, such as k-Modes and k-Prototypes. - **Clustering Multimedia Data**: Explores clustering techniques for multimedia data, including images, videos, and audio. - **Clustering Time-Series Data**: Introduces methods for clustering time-series data, such as correlation-based and shape-based approaches. - **Clustering Biological Data**: Discusses clustering techniques for biological data, including microarray data and biological networks. - **Clustering Network Data**: Focuses on clustering methods for network data, such as graph-theoretic approaches. - **Clustering Uncertain Data**: Introduces methods for handling uncertain data, including mixture models and density-based approaches. 2. **Domain Chapters**: - **Categorical Data Clustering**: Discusses specific methods for categorical data, such as k-Modes and k-Prototypes. - **Text Data Clustering**: Explains techniques for clustering text data, including term frequency and probabilistic models. - **Multimedia Data Clustering**: Introduces methods for clustering multimedia data, such as image and video data. - **Graph Data Clustering**: Discusses clustering methods for graph data, including spectral and geometric approaches. - **Biological Data Clustering**: Focuses on clusteringThe book "Data Clustering: Algorithms and Applications" edited by Charu C. Aggarwal and Chandan K. Reddy provides a comprehensive overview of the field of data clustering, covering both core methods and specific applications. The book is structured into three main sections: Method Chapters, Domain Chapters, and Variations and Insights. 1. **Method Chapters**: - **Feature Selection Methods**: Discusses techniques for selecting relevant features to enhance clustering quality. - **Probabilistic and Generative Models**: Explains how to model data using probabilistic models and generative processes, often solved with the EM algorithm. - **Distance-Based Algorithms**: Introduces methods that use distance functions to partition data, including flat and divisive approaches. - **Density-Based Clustering**: Focuses on algorithms that identify clusters based on density, such as DBSCAN. - **Grid-Based Clustering**: Describes methods that use grid structures to cluster data, like STING and CLIQUE. - **Spectral Clustering**: Explains how to use spectral methods to find clusters in data represented by a similarity graph. - **Nonnegative Matrix Factorizations (NMF)**: Discusses NMF for clustering, including its theoretical foundations and practical applications. - **Clustering High-Dimensional Data**: Addressing challenges and techniques for clustering high-dimensional data. - **Clustering Stream Data**: Introduces methods for streaming data, such as k-Means and streaming algorithms. - **Big Data Clustering**: Covers techniques for handling large datasets, including one-pass algorithms and parallel methods. - **Clustering Categorical Data**: Discusses methods for categorical data, such as k-Modes and k-Prototypes. - **Clustering Multimedia Data**: Explores clustering techniques for multimedia data, including images, videos, and audio. - **Clustering Time-Series Data**: Introduces methods for clustering time-series data, such as correlation-based and shape-based approaches. - **Clustering Biological Data**: Discusses clustering techniques for biological data, including microarray data and biological networks. - **Clustering Network Data**: Focuses on clustering methods for network data, such as graph-theoretic approaches. - **Clustering Uncertain Data**: Introduces methods for handling uncertain data, including mixture models and density-based approaches. 2. **Domain Chapters**: - **Categorical Data Clustering**: Discusses specific methods for categorical data, such as k-Modes and k-Prototypes. - **Text Data Clustering**: Explains techniques for clustering text data, including term frequency and probabilistic models. - **Multimedia Data Clustering**: Introduces methods for clustering multimedia data, such as image and video data. - **Graph Data Clustering**: Discusses clustering methods for graph data, including spectral and geometric approaches. - **Biological Data Clustering**: Focuses on clustering