Seriation in Combinatorial and Statistical Data Analysis
Seriation in Combinatorial and Statistical Data Analysis

Author: Israël César Lerman

Publisher: Springer Nature

Published: 2022-03-04

Total Pages: 287

ISBN-13: 303092694X

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This monograph offers an original broad and very diverse exploration of the seriation domain in data analysis, together with building a specific relation to clustering. Relative to a data table crossing a set of objects and a set of descriptive attributes, the search for orders which correspond respectively to these two sets is formalized mathematically and statistically. State-of-the-art methods are created and compared with classical methods and a thorough understanding of the mutual relationships between these methods is clearly expressed. The authors distinguish two families of methods: Geometric representation methods Algorithmic and Combinatorial methods Original and accurate methods are provided in the framework for both families. Their basis and comparison is made on both theoretical and experimental levels. The experimental analysis is very varied and very comprehensive. Seriation in Combinatorial and Statistical Data Analysis has a unique character in the literature falling within the fields of Data Analysis, Data Mining and Knowledge Discovery. It will be a valuable resource for students and researchers in the latter fields.

Foundations and Methods in Combinatorial and Statistical Data Analysis and Clustering
Foundations and Methods in Combinatorial and Statistical Data Analysis and Clustering

Author: Israël César Lerman

Publisher: Springer

Published: 2016-03-24

Total Pages: 664

ISBN-13: 1447167937

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This book offers an original and broad exploration of the fundamental methods in Clustering and Combinatorial Data Analysis, presenting new formulations and ideas within this very active field. With extensive introductions, formal and mathematical developments and real case studies, this book provides readers with a deeper understanding of the mutual relationships between these methods, which are clearly expressed with respect to three facets: logical, combinatorial and statistical. Using relational mathematical representation, all types of data structures can be handled in precise and unified ways which the author highlights in three stages: Clustering a set of descriptive attributes Clustering a set of objects or a set of object categories Establishing correspondence between these two dual clusterings Tools for interpreting the reasons of a given cluster or clustering are also included. Foundations and Methods in Combinatorial and Statistical Data Analysis and Clustering will be a valuable resource for students and researchers who are interested in the areas of Data Analysis, Clustering, Data Mining and Knowledge Discovery.

Combinatorial Data Analysis
Combinatorial Data Analysis

Author: Lawrence Hubert

Publisher: SIAM

Published: 2001-01-01

Total Pages: 174

ISBN-13: 9780898718553

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Combinatorial data analysis (CDA) refers to a wide class of methods for the study of relevant data sets in which the arrangement of a collection of objects is absolutely central. The focus of this monograph is on the identification of arrangements, which are then further restricted to where the combinatorial search is carried out by a recursive optimization process based on the general principles of dynamic programming (DP).

Branch-and-Bound Applications in Combinatorial Data Analysis
Branch-and-Bound Applications in Combinatorial Data Analysis

Author: Michael J. Brusco

Publisher: Springer Science & Business Media

Published: 2005-11-30

Total Pages: 222

ISBN-13: 0387288104

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This book provides clear explanatory text, illustrative mathematics and algorithms, demonstrations of the iterative process, pseudocode, and well-developed examples for applications of the branch-and-bound paradigm to important problems in combinatorial data analysis. Supplementary material, such as computer programs, are provided on the world wide web. Dr. Brusco is an editorial board member for the Journal of Classification, and a member of the Board of Directors for the Classification Society of North America.

Statistical Models for Data Analysis
Statistical Models for Data Analysis

Author: Paolo Giudici

Publisher: Springer Science & Business Media

Published: 2013-07-01

Total Pages: 413

ISBN-13: 3319000322

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The papers in this book cover issues related to the development of novel statistical models for the analysis of data. They offer solutions for relevant problems in statistical data analysis and contain the explicit derivation of the proposed models as well as their implementation. The book assembles the selected and refereed proceedings of the biannual conference of the Italian Classification and Data Analysis Group (CLADAG), a section of the Italian Statistical Society. ​

Assignment Methods in Combinational Data Analysis
Assignment Methods in Combinational Data Analysis

Author: Lawrence Hubert

Publisher: CRC Press

Published: 1986-09-29

Total Pages: 350

ISBN-13: 9780824776176

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For the first time in one text, this handy pedagogical reference presents comprehensive inference strategies for organizing disparate nonparametric statistics topics under one scheme, illustrating ways of analyzing data sets based on generic notions of proximity (of "closeness") between objects. Assignment Methods in Combinatorial Data Analysis specifically reviews both linear and quadratic assignment models ... covers extensions to multiple object sets and higher-order assignment indices ... considers methods of applying linear assignment models in common data analysis contexts ... discusses a second motion of assignment (or "matching") based upon pairs of objects ... explores confirmatory methods of augmenting multidimensional sealing, cluster analysis, and related techniques ... labels sections in order of priority for continuity and convenience ... and includes extensive bibliographies of related literature. Assignment Methods in Combinatorial Data Analysis gives authoritative coverage of statistical testing, and measures of association in a single source. It is required reading and an invaluable reference for researchers and graduate students in the behavioral and social sciences using quantitative methods of data representation. Book jacket.

Statistics in the Social Sciences
Statistics in the Social Sciences

Author: Stanislav Kolenikov

Publisher: John Wiley & Sons

Published: 2010-02-22

Total Pages: 222

ISBN-13: 0470583320

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A one-of-a-kind compilation of modern statistical methods designed to support and advance research across the social sciences Statistics in the Social Sciences: Current Methodological Developments presents new and exciting statistical methodologies to help advance research and data analysis across the many disciplines in the social sciences. Quantitative methods in various subfields, from psychology to economics, are under demand for constant development and refinement. This volume features invited overview papers, as well as original research presented at the Sixth Annual Winemiller Conference: Methodological Developments of Statistics in the Social Sciences, an international meeting that focused on fostering collaboration among mathematical statisticians and social science researchers. The book provides an accessible and insightful look at modern approaches to identifying and describing current, effective methodologies that ultimately add value to various fields of social science research. With contributions from leading international experts on the topic, the book features in-depth coverage of modern quantitative social sciences topics, including: Correlation Structures Structural Equation Models and Recent Extensions Order-Constrained Proximity Matrix Representations Multi-objective and Multi-dimensional Scaling Differences in Bayesian and Non-Bayesian Inference Bootstrap Test of Shape Invariance across Distributions Statistical Software for the Social Sciences Statistics in the Social Sciences: Current Methodological Developments is an excellent supplement for graduate courses on social science statistics in both statistics departments and quantitative social sciences programs. It is also a valuable reference for researchers and practitioners in the fields of psychology, sociology, economics, and market research.

FUTURE TRENDS IN BLOCKCHAIN SCALABILITY, INTEROPARABILITY, AND BEYOND MACHINE LEARNING
FUTURE TRENDS IN BLOCKCHAIN SCALABILITY, INTEROPARABILITY, AND BEYOND MACHINE LEARNING

Author: Manoj Ram Tammina

Publisher: Xoffencerpublication

Published: 2023-10-30

Total Pages: 222

ISBN-13: 8119534557

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A blockchain is a distributed public ledger that records transactions in a series of linked blocks that can be accessed by anyone. Before being added to the chain, the data (block) is time-stamped and verified. Each block builds upon the data in the one before it. The data is very difficult to forge due to the mathematical complexity of the storage system. The legacy of cryptocurrencies has helped turn the cryptographic term "blockchain" into a trendy catchphrase. A lot of people think blockchain is the same thing as cryptocurrencies. The opposite is true. Blockchain is the underlying technology behind cryptocurrencies, but its uses extend well beyond that. Blockchains may be considered for use in situations requiring the validation, auditing, or exchange of data. Here, we survey the literature on integrating blockchain with machine learning, and show that the two may work together successfully and efficiently. Machine learning is an umbrella word that includes a wide range of techniques, such as traditional ML, DL, and RL. As a distributed and append-only ledger system, the blockchain is a natural instrument for sharing and processing large data from multiple sources thanks to the inclusion of smart contracts, which is a crucial component of the infrastructure necessary for big data analysis. When it comes to training and testing machine learning models, blockchain can keep data secure and promote data exchange. In addition, it paves the way for the creation of timely prediction models using several data sources by leveraging distributed computing resources (like IoT). This is crucial for deep learning processes, which need a lot of processing time. However, distributed systems are more difficult to monitor and regulate than centralized ones, and blockchain systems will create a massive quantity of data from a variety of sources. The best blockchain mechanism designs need accurate data analysis and predictions of system behaviors. Data verification, as well as the detection of harmful assaults and dishonest transactions on the blockchain, may be aided by machine learning. There is a lot to gain from studying how to merge the two technologies from different perspectives.