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Author: Israël César Lerman
Publisher: Springer Nature
ISBN: 303092694X
Category : Computers
Languages : en
Pages : 287
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Book Description
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.
Author: Israël César Lerman
Publisher: Springer Nature
ISBN: 303092694X
Category : Computers
Languages : en
Pages : 287
Get Book Here
Book Description
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.
Author: Israël César Lerman
Publisher: Springer
ISBN: 1447167937
Category : Computers
Languages : en
Pages : 664
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Book Description
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.
Author: Lawrence Hubert
Publisher: SIAM
ISBN: 9780898718553
Category : Science
Languages : en
Pages : 174
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Book Description
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).
Author: Paolo Giudici
Publisher: Springer Science & Business Media
ISBN: 3319000322
Category : Mathematics
Languages : en
Pages : 413
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Book Description
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.
Author: Michael J. Brusco
Publisher: Springer Science & Business Media
ISBN: 9780387250373
Category : Business & Economics
Languages : en
Pages : 248
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Book Description
There are a variety of combinatorial optimization problems that are relevant to the examination of statistical data. Combinatorial problems arise in the clustering of a collection of objects, the seriation (sequencing or ordering) of objects, and the selection of variables for subsequent multivariate statistical analysis such as regression. The options for choosing a solution strategy in combinatorial data analysis can be overwhelming. Because some problems are too large or intractable for an optimal solution strategy, many researchers develop an over-reliance on heuristic methods to solve all combinatorial problems. However, with increasingly accessible computer power and ever-improving methodologies, optimal solution strategies have gained popularity for their ability to reduce unnecessary uncertainty. In this monograph, optimality is attained for nontrivially sized problems via the branch-and-bound paradigm. For many combinatorial problems, branch-and-bound approaches have been proposed and/or developed. However, until now, there has not been a single resource in statistical data analysis to summarize and illustrate available methods for applying the branch-and-bound process. This monograph provides clear explanatory text, illustrative mathematics and algorithms, demonstrations of the iterative process, psuedocode, 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 a Professor of Marketing and Operations Research at Florida State University, an editorial board member for the Journal of Classification, and a member of the Board of Directors for the Classification Society of North America. Stephanie Stahl is an author and researcher with years of experience in writing, editing, and quantitative psychology research.
Author: Reginald G. Golledge
Publisher: U of Minnesota Press
ISBN: 1452911320
Category :
Languages : en
Pages : 357
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Book Description
Author: Michael J. Brusco
Publisher: Springer Science & Business Media
ISBN: 0387288104
Category : Mathematics
Languages : en
Pages : 222
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Book Description
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.
Author:
Publisher:
ISBN:
Category : City planning
Languages : en
Pages : 896
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Book Description
Author: Manoj Ram Tammina
Publisher: Xoffencerpublication
ISBN: 8119534557
Category : Business & Economics
Languages : en
Pages : 222
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Book Description
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.
Author:
Publisher:
ISBN:
Category : Psychology
Languages : en
Pages : 266
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Book Description
