Open access peer-reviewed Edited Volume

Data Clustering

Niansheng Tang

Yunnan University

Professor Niansheng Tang is a member of the International Statistical Institute (ISI) and a member of the board of the International Chinese Statistical Association (ICSA).

Covering

Big Data Clustering Algorithm Generic Data High Dimensional Data Model-Based Clustering Model Free Clustering Large Scale Time Series Feature Selection Latent Class Analysis Bayesian Method

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About the book

Clustering is the process of grouping the data individuals into classes or clusters of similar individuals. A cluster is a collection of data individuals that are similar to one another within the same cluster and are dissimilar to the individuals in other clusters. Clustering can be employed to obtain the distribution of data, the features of each cluster, and analyze some special clusters. A lot of algorithms have been developed for data clustering. The existing clustering algorithms include partitional clustering (e.g., K-means, K-medoids), hierarchical clustering, density-based clustering, and model-based clustering. However, these exiting methods can not directly be applied to high-dimensional data clustering, large scale data clustering, and distributed data clustering, and big data clustering. To this end, the aims of the project are to develop some novel methods and algorithms to conduct data clustering for various data, including generic data, time-series data, big data, large scale data, high-dimensional and missing data, and investigate their theoretical properties under some wild conditions. In particular, some novel dimension reduction techniques and model average techniques, and Bayesian tools are developed to conduct data clustering for some complicated data.
 

Publishing process

Book initiated and editor appointed

Date completed: May 3rd 2021

Applications to edit the book are assessed and a suitable editor is selected, at which point the process begins.

Chapter proposals submitted and reviewed

Deadline for chapter proposals: May 31st 2021

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Approved chapters written in full and submitted

Deadline for full chapters: July 30th 2021

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Full chapters peer reviewed

Review results due: October 18th 2021

Full chapter manuscripts are screened for plagiarism and undergo a Main Editor Peer Review. Results are sent to authors within 30 days of submission, with suggestions for rounds of revisions.

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Expected publication date: December 17th 2021

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About the editor

Niansheng Tang

Yunnan University

Niansheng Tang is a Professor of Statistics and dean of the School of Mathematics and Statistics, Yunnan University. He was elected a Yangtze River Scholars Distinguished Professor in 2013, a member of the International Statistical Institute (ISI) in 2016, and a member of the board of International Chinese Statistical Association (ICSA) in 2018. He obtained the National Science Foundation for Distinguished Young Scholars of China in 2012 and was awarded the Outstanding Service Award of the ICSA in 2018. He serves as a member of the editorial board for Statistics and Its Interface, Journal of Systems Science and Complexity, Statistical Theory and Related Fields, BMC Medical Research Methodology. He is also an Editor in Field for Communications in Mathematics and Statistics. His research interests include data clustering, biostatistics, Bayesian statistics, missing data analysis, statistical diagnosis, variable selection, and high-dimensional data analysis. He has published more than 175 research papers and authored four books.

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Book chapters authored 1

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