**Preface XI**


Preface

represent:

We are experiencing a transition from an information age to a wisdom age driven by an explosion in data available for analysis. A major consequence of this transition is an evolu‐ tion of data mining to become data analytics, a discipline involving engineers, statisticians, and computer scientists together with the diverse realms they serve. The potential value that resides in data is suggested in the famous saying "data is the oil of the age," but suitable

Clustering has emerged as one of the more fertile fields within data analytics, widely adopt‐ ed by companies, research institutions, and educational entities as a tool to describe similar/ different groups, communities, patterns, modules, and objects and broadly to predict assign‐ ment of certain members to unlabeled groups in an unsupervised fashion. Often classed as an instance of machine learning, clustering has found applications to generate groups con‐ sisting of market segments, genes, constellations of stars, movies to recommend, facilities to

The history of clustering dates back to ancient times, manifest in Aristotles' taxonomy of living things, and quite possibly can be traced even earlier. Just as counting is essential to computation, clustering is essential for learning and predicting. Hence, clustering algo‐

This book is intended to provide a view of recent contributions to the vast clustering litera‐ ture 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. Image, text, face recognition, speech (synthetic and simulated), and smart city datasets are used. The ta‐ ble below is a summary of chapters according to the types of theory and applications they

1 Missing values, K-means, mean shift Six datasets, speech and image

processing unit

İmage clustering

theories and tools are needed to tease this value into the open.

serve critical functions, and remarkable communities within a society.

**Chapter Theory Applications**

3 KL divergence C-means İmage clustering 4 K-means, semantic similarity Text fragmentation

2 K-means, C-means, colored C-means, genetic

algorithm

rithms have been developed in rich abundance.

Chapter 12 **Collective Solutions on Sets of Stable Clusterings 221** Vladimir Vasilevich Ryazanov
