"Recent Applications in Data Clustering" ed. by Harun Pirim

The book aims to provide an outlook of recent contributions to the vast clustering literature that offers useful insights within the context of modern applications for professionals, academics, and students. The book spans the domains of clustering in image analysis, lexical analysis of texts, replacement of missing values in data, temporal clustering in smart cities, comparison of artificial neural network variations, graph theoretical approaches, spectral clustering, multiview clustering, and model-based clustering in an R package.

Clustering has emerged as one of the more fertile fields within data analytics, widely adopted by companies, research institutions, and educational entities as a tool to describe similar/different groups.
Applications of image, text, face recognition, speech (synthetic and simulated), and smart city datasets are presented.

Contents
1 Clustering Algorithms for Incomplete Datasets
2 Partitional Clustering
3 Incorporating Local Data and KL Membership Divergence into Hard C-Means Clustering for Fuzzy and Noise-Robust Data Segmentation
4 Centroid-Based Lexical Clustering
5 Point Cloud Clustering Using Panoramic Layered Range Image
6 CoClust: An R Package for Copula-Based Cluster Analysis
7 Temporal Clustering for Behavior Variation and Anomaly Detection from Data Acquired Through loT in Smart Cities
8 A Class of Parametric Tree-Based Clustering Methods
9 Robust Spectral Clustering via Sparse Representation
10 Performance Assessment of Unsupervised Clustering Algorithms Combined MDL Index
11 New Approaches in Multi-View Clustering
12 Collective Solutions on Sets of Stable Clusterings