Fionn Murtagh

Roberto Rocci

Christian Hennig

Marina Meila

Handbook of Cluster Analysis provides a comprehensive and unified account of the main research developments in cluster analysis. Written by active, distinguished researchers in this area, the book helps readers make informed choices of the most suitable clustering approach for their problem and make better use of existing cluster analysis tools. The book is organized according to the traditional core approaches to cluster analysis, from the origins to recent developments. After an overview of approaches and a quick journey through the history of cluster analysis, the book focuses on the four major approaches to cluster analysis. These approaches include methods for optimizing an objective function that describes how well data is grouped around centroids, dissimilarity-based methods, mixture models and partitioning models, and clustering methods inspired by nonparametric density estimation. The book also describes additional approaches to cluster analysis, including constrained and semi-supervised clustering, and explores other relevant issues, such as evaluating the quality of a cluster. This handbook is accessible to readers from various disciplines, reflecting the interdisciplinary nature of cluster analysis. For those already experienced with cluster analysis, the book offers a broad and structured overview. For newcomers to the field, it presents an introduction to key issues. For researchers who are temporarily or marginally involved with cluster analysis problems, the book gives enough algorithmic and practical details to facilitate working knowledge of specific clustering areas.

Handbook of Cluster Analysis

Mireille Gettler Summa

Leon Bottou

Bernard Goldfarb

Catherine Pardoux

Myriam Touati

Data analysis is changing fast. Driven by a vast range of application domains and affordable tools, machine learning has become mainstream. Unsupervised data analysis, including cluster analysis, factor analysis, and low dimensionality mapping methods continually being updated, have reached new heights of achievement in the incredibly rich data wor

Statistical Learning and Data Science

Patrice Bellot

Chiraz Trabelsi

Josiane Mothe

Jian Yun Nie

Laure Soulier

Eric SanJuan

Linda Cappellato

Nicola Ferro

This book constitutes the refereed proceedings of the 9th International Conference of the CLEF Initiative, CLEF 2018, jointly organized by Avignon, Marseille and Toulon universities and held in Avignon, France, in September 2018. The conference has a clear focus on experimental information retrieval with special attention to the challenges of multimodality, multilinguality, and interactive search ranging from unstructured to semi structures and structured data. The 13 papers presented in this volume were carefully reviewed and selected from 39 submissions. Many papers tackle the medical ehealth and ehealth multimedia retrieval challenges, however there are many other topics of research such as document clustering, social biases in IR, social book search, personality profiling. Further this volume presents 9 “best of the labs” papers which were reviewed as a full paper submission with the same review criteria. The labs represented scientific challenges based on new data sets and real world problems in multimodal and multilingual information access. In addition to this, 10 benchmarking labs reported results of their yearlong activities in overview talks and lab sessions. The papers address all aspects of information access in any modularity and language and cover a broad range of topics in the field of multilingual and multimodal information access evaluation.

9th International Conference of the CLEF Association, CLEF 2018, Avignon, France, September 10-14, 2018, Proceedings

Experimental IR Meets Multilinguality, Multimodality, and Interaction

Fionn Murtagh

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