This book provides the reader with a basic understanding of the formal concepts of the cluster, clustering, partition, cluster analysis etc. The book explains feature-based, graph-based and spectral clustering methods and discusses their formal similarities and differences. Understanding the related formal concepts is particularly vital in the epoch of Big Data; due to the volume and characteristics of the data, it is no longer feasible to predominantly rely on merely viewing the data when facing a clustering problem. Usually clustering involves choosing similar objects and grouping them together. To facilitate the choice of similarity measures for complex and big data, various measures of object similarity, based on quantitative (like numerical measurement results) and qualitative features (like text), as well as combinations of the two, are described, as well as graph-based similarity measures for (hyper) linked objects and measures for multilayered graphs. Numerous variants demonstrating how such similarity measures can be exploited when defining clustering cost functions are also presented. In addition, the book provides an overview of approaches to handling large collections of objects in a reasonable time. In particular, it addresses grid-based methods, sampling methods, parallelization via Map-Reduce, usage of tree-structures, random projections and various heuristic approaches, especially those used for community detection.
This book provides the reader with a basic understanding of the formal concepts of the cluster, clustering, partition, cluster analysis etc.
Author: Slawomir Wierzchoń
This 2 volume-set of IFIP AICT 583 and 584 constitutes the refereed proceedings of the 16th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2020, held in Neos Marmaras, Greece, in June 2020.* The 70 full papers and 5 short papers presented were carefully reviewed and selected from 149 submissions. They cover a broad range of topics related to technical, legal, and ethical aspects of artificial intelligence systems and their applications and are organized in the following sections: Part I: classification; clustering - unsupervised learning -analytics; image processing; learning algorithms; neural network modeling; object tracking - object detection systems; ontologies - AI; and sentiment analysis - recommender systems. Part II: AI ethics - law; AI constraints; deep learning - LSTM; fuzzy algebra - fuzzy systems; machine learning; medical - health systems; and natural language. *The conference was held virtually due to the COVID-19 pandemic.
The incremental k-means algorithm returns only a set of cluster centers without
stating whether or not we got a perfect ball clustering. However, if we are ...
Wierzchon, S.T., Klopotek, M.A.: Modern Algorithms of Cluster Analysis. Studies
in Big ...
Author: Ilias Maglogiannis
Publisher: Springer Nature
Table 1.1 Differences among standard, fuzzy, and model-based approaches to
clustering Feature Standard approach Fuzzy approach Model-based approach ...
Klopotek, M.A., Wierzcho ́n, S.T.: Modern Algorithms of Cluster Analysis. Springer
Author: Paolo Giordani
Publisher: Springer Nature
Presents the latest techniques for analyzing and extracting information from large amounts of data in high-dimensional data spaces The revised and updated third edition of Data Mining contains in one volume an introduction to a systematic approach to the analysis of large data sets that integrates results from disciplines such as statistics, artificial intelligence, data bases, pattern recognition, and computer visualization. Advances in deep learning technology have opened an entire new spectrum of applications. The author—a noted expert on the topic—explains the basic concepts, models, and methodologies that have been developed in recent years. This new edition introduces and expands on many topics, as well as providing revised sections on software tools and data mining applications. Additional changes include an updated list of references for further study, and an extended list of problems and questions that relate to each chapter.This third edition presents new and expanded information that: • Explores big data and cloud computing • Examines deep learning • Includes information on convolutional neural networks (CNN) • Offers reinforcement learning • Contains semi-supervised learning and S3VM • Reviews model evaluation for unbalanced data Written for graduate students in computer science, computer engineers, and computer information systems professionals, the updated third edition of Data Mining continues to provide an essential guide to the basic principles of the technology and the most recent developments in the field.
Anderson C., D. Lee, N. Dean, Spatial Clustering of Average Risks and Risk
Trends in Bayesian Disease Mapping, Biometrical Journal, Vol. ... Slawomir
Wierzchon, Mieczyslaw Kłopotek, Modern Algorithms of Cluster Analysis,
Author: Mehmed Kantardzic
Publisher: John Wiley & Sons
This accessible, alphabetical guide provides concise insights into a variety of digital research methods, incorporating introductory knowledge with practical application and further research implications. A-Z of Digital Research Methods provides a pathway through the often-confusing digital research landscape, while also addressing theoretical, ethical and legal issues that may accompany each methodology. Dawson outlines 60 chapters on a wide range of qualitative and quantitative digital research methods, including textual, numerical, geographical and audio-visual methods. This book includes reflection questions, useful resources and key texts to encourage readers to fully engage with the methods and build a competent understanding of the benefits, disadvantages and appropriate usages of each method. A-Z of Digital Research Methods is the perfect introduction for any student or researcher interested in digital research methods for social and computer sciences.
Grekousis, G. and Hatzichristos, T. (2013) 'Fuzzy Clustering Analysis in
Geomarketing Research', Environment and Planning B: Urban Analytics and City
Science ... Wierzchoń, S. and Klopotek, M. (2018) Modern Algorithms of Cluster
Author: Catherine Dawson
Category: Social Science
Data has increased due to the growing use of web applications and communication devices. It is necessary to develop new techniques of managing data in order to ensure adequate usage. Modern Technologies for Big Data Classification and Clustering is an essential reference source for the latest scholarly research on handling large data sets with conventional data mining and provide information about the new technologies developed for the management of large data. Featuring coverage on a broad range of topics such as text and web data analytics, risk analysis, and opinion mining, this publication is ideally designed for professionals, researchers, and students seeking current research on various concepts of big data analytics.
Data has increased due to the growing use of web applications and communication devices. It is necessary to develop new techniques of managing data in order to ensure adequate usage.
Author: Seetha, Hari
Publisher: IGI Global
Recently many researchers are working on cluster analysis as a main tool for exploratory data analysis and data mining. A notable feature is that specialists in di?erent ?elds of sciences are considering the tool of data clustering to be useful. A major reason is that clustering algorithms and software are ?exible in thesensethatdi?erentmathematicalframeworksareemployedinthealgorithms and a user can select a suitable method according to his application. Moreover clusteringalgorithmshavedi?erentoutputsrangingfromtheolddendrogramsof agglomerativeclustering to more recent self-organizingmaps. Thus, a researcher or user can choose an appropriate output suited to his purpose,which is another ?exibility of the methods of clustering. An old and still most popular method is the K-means which use K cluster centers. A group of data is gathered around a cluster center and thus forms a cluster. The main subject of this book is the fuzzy c-means proposed by Dunn and Bezdek and their variations including recent studies. A main reasonwhy we concentrate on fuzzy c-means is that most methodology and application studies infuzzy clusteringusefuzzy c-means,andfuzzy c-meansshouldbe consideredto beamajortechniqueofclusteringingeneral,regardlesswhetheroneisinterested in fuzzy methods or not. Moreover recent advances in clustering techniques are rapid and we requirea new textbook that includes recent algorithms.We should also note that several books have recently been published but the contents do not include some methods studied herein.
A group of data is gathered around a cluster center and thus forms a cluster. The main subject of this book is the fuzzy c-means proposed by Dunn and Bezdek and their variations including recent studies.
Author: Sadaaki Miyamoto
Publisher: Springer Science & Business Media
This is the first book on multivariate analysis to look at large data sets which describes the state of the art in analyzing such data. Material such as database management systems is included that has never appeared in statistics books before.
We discuss multivariate analysis of variance and multivariate reduced-rank
regression (RRR). RRR provides the ... Chapter 12 describes the many
algorithms for cluster analysis and unsupervised learning. In Chapter 13, we
Author: Alan J. Izenman
Publisher: Springer Science & Business Media
Most modern textbooks on cluster analysis are written from the standpoint of computer science, which give the background, description and implementation of computer algorithms. This book proclaims several firsts — the first to present a broad mathematical treatment of the subject, the first that illustrates dissimilarities taking values in a poset, and the first to notice the connection with formal concept analysis which is a powerful tool for investigating hidden structures in large data sets. This book presents the subject from a mathematical viewpoint with careful definitions. All clearly stated axioms are illustrated with concrete examples. New ideas are introduced informally first, and then in a careful, systematic manner. Much of the material has not previously appeared in the literature. It is to be hoped that the book holds promising directive to launch a new research area that is based on graph theory, as well as partially ordered sets. It also suggests the cluster algorithms that can be used for practical applications. The emphasis will be largely on ordinal data and ordinal cluster methods. CD Contents (177 KB)
This book proclaims several firsts — the first to present a broad mathematical treatment of the subject, the first that illustrates dissimilarities taking values in a poset, and the first to notice the connection with formal concept ...
Author: Melvin F Janowitz
Publisher: World Scientific Publishing Company
Clustering is one of the most fundamental and essential data analysis techniques. Clustering can be used as an independent data mining task to discern intrinsic characteristics of data, or as a preprocessing step with the clustering results then used for classification, correlation analysis, or anomaly detection. Kogan and his co-editors have put together recent advances in clustering large and high-dimension data. Their volume addresses new topics and methods which are central to modern data analysis, with particular emphasis on linear algebra tools, opimization methods and statistical techniques. The contributions, written by leading researchers from both academia and industry, cover theoretical basics as well as application and evaluation of algorithms, and thus provide an excellent state-of-the-art overview. The level of detail, the breadth of coverage, and the comprehensive bibliography make this book a perfect fit for researchers and graduate students in data mining and in many other important related application areas.
The level of detail, the breadth of coverage, and the comprehensive bibliography make this book a perfect fit for researchers and graduate students in data mining and in many other important related application areas.
Author: Jacob Kogan
Publisher: Springer Science & Business Media
This volume, representing a compilation of authoritative reviews on a multitude of uses of statistics in epidemiology and medical statistics written by internationally renowned experts, is addressed to statisticians working in biomedical and epidemiological fields who use statistical and quantitative methods in their work. While the use of statistics in these fields has a long and rich history, explosive growth of science in general and clinical and epidemiological sciences in particular have gone through a see of change, spawning the development of new methods and innovative adaptations of standard methods. Since the literature is highly scattered, the Editors have undertaken this humble exercise to document a representative collection of topics of broad interest to diverse users. The volume spans a cross section of standard topics oriented toward users in the current evolving field, as well as special topics in much need which have more recent origins. This volume was prepared especially keeping the applied statisticians in mind, emphasizing applications-oriented methods and techniques, including references to appropriate software when relevant. · Contributors are internationally renowned experts in their respective areas · Addresses emerging statistical challenges in epidemiological, biomedical, and pharmaceutical research · Methods for assessing Biomarkers, analysis of competing risks · Clinical trials including sequential and group sequential, crossover designs, cluster randomized, and adaptive designs · Structural equations modelling and longitudinal data analysis
Abstract This chapter introduces cluster analysis algorithms for finding subgroups
of objects (e.g., patients, genes) in data such ... and motivate this work, it is
valuable to have a basic overview of some modern statistical clustering
Given a set of N points and distances between all points, the paper presents an algorithm for determining an optimal partition of the points into k mutually exclusive and exhaustive subsets or clusters according to an objective function defined on the set of all partitions. The value of the objective function for a given partition is defined as the maximum within-cluster distance in the partition. The algorithm determines an optimal partition by solving a sequence of set-covering problems. The set-covering problems have no more than N constraints and typically less than 1.5N variables. (Author).
Similarly , if women adopting modern techniques of birth control had been
employing traditional schemes of limited , but positive , effectiveness , the impact
of the program on the birth rate would be only a fraction of that implied by the ...
Author: Chris Roach
Category: Cluster analysis
Collection of selected, peer reviewed papers from the 2013 2nd International Conference on Mechanical Properties of Materials and Information Technology (ICMPMIT 2013), August 17-19, 2013, Hong Kong. The 133 papers are grouped as follows: Chapter 1: Applied Materials and Technology of Processing; Chapter 2: Materials and Technologies in Micro- and Optoelectronics; Chapter 3: Materials and Technologies in Construction; Chapter 4: Materials and Technologies in Environmental Engineering; Chapter 5: Technologies of Applied Design in Industry; Chapter 6: Measurement Technologies, Signal and Data Processing; Chapter 7: Design and Research of MEMS; Chapter 8: Communication, Control and Information Technology in Engineering; Chapter 9: Power Systems and Power Engineering; Chapter 10: Other Related Topics.
Collection of selected, peer reviewed papers from the 2013 2nd International Conference on Mechanical Properties of Materials and Information Technology (ICMPMIT 2013), August 17-19, 2013, Hong Kong.
Author: Zhang Jun
Publisher: Trans Tech Publications Ltd
Category: Technology & Engineering
One of the grand challenges in our digital world are the large, complex and often weakly structured data sets, and massive amounts of unstructured information. This “big data” challenge is most evident in biomedical informatics: the trend towards precision medicine has resulted in an explosion in the amount of generated biomedical data sets. Despite the fact that human experts are very good at pattern recognition in dimensions of = 3; most of the data is high-dimensional, which makes manual analysis often impossible and neither the medical doctor nor the biomedical researcher can memorize all these facts. A synergistic combination of methodologies and approaches of two fields offer ideal conditions towards unraveling these problems: Human–Computer Interaction (HCI) and Knowledge Discovery/Data Mining (KDD), with the goal of supporting human capabilities with machine learning./ppThis state-of-the-art survey is an output of the HCI-KDD expert network and features 19 carefully selected and reviewed papers related to seven hot and promising research areas: Area 1: Data Integration, Data Pre-processing and Data Mapping; Area 2: Data Mining Algorithms; Area 3: Graph-based Data Mining; Area 4: Entropy-Based Data Mining; Area 5: Topological Data Mining; Area 6 Data Visualization and Area 7: Privacy, Data Protection, Safety and Security.
Keywords: Open medical data, knowledge discovery, biomedical data mining,
bacteria, drug adverse event, erythromycin, cluster analysis, clustering algorithms
. 1 Introduction Modern technology has increased the power of data by facilitating
Author: Andreas Holzinger
Discover hidden relationships among the variables in your data, and learn how to exploit these relationships. This book presents a collection of data-mining algorithms that are effective in a wide variety of prediction and classification applications. All algorithms include an intuitive explanation of operation, essential equations, references to more rigorous theory, and commented C++ source code. Many of these techniques are recent developments, still not in widespread use. Others are standard algorithms given a fresh look. In every case, the focus is on practical applicability, with all code written in such a way that it can easily be included into any program. The Windows-based DATAMINE program lets you experiment with the techniques before incorporating them into your own work. What You'll Learn Use Monte-Carlo permutation tests to provide statistically sound assessments of relationships present in your data Discover how combinatorially symmetric cross validation reveals whether your model has true power or has just learned noise by overfitting the data Work with feature weighting as regularized energy-based learning to rank variables according to their predictive power when there is too little data for traditional methods See how the eigenstructure of a dataset enables clustering of variables into groups that exist only within meaningful subspaces of the data Plot regions of the variable space where there is disagreement between marginal and actual densities, or where contribution to mutual information is high Who This Book Is For Anyone interested in discovering and exploiting relationships among variables. Although all code examples are written in C++, the algorithms are described in sufficient detail that they can easily be programmed in any language.
This book also covers information entropy, permutation tests, combinatorics, predictor selections, and eigenvalues to give you a well-rounded view of data mining and algorithms in C++.
Author: Timothy Masters
"This book provides a compendium of terms, definitions, and explanations of concepts in various areas of systems and design, as well as a vast collection of cutting-edge research articles from the field's leading experts"--Provided by publisher.
The entire amount of medical data (i.e., data, text, and images features) is
introduced to each clustering algorithm in the ... Mining clustering Methods
Regurality Report CART SOFM SOM Neural Network Trend Analysis Patient
Diagnosis and ...
Author: Syed, Mahbubur Rahman
Publisher: IGI Global
This book is a comprehensive introduction to the methods and algorithms and approaches of modern data analytics. It covers data preprocessing, visualization, correlation, regression, forecasting, classification, and clustering. It provides a sound mathematical basis, discusses advantages and drawbacks of different approaches, and enables the reader to design and implement data analytics solutions for real-world applications. The text is designed for undergraduate and graduate courses on data analytics for engineering, computer science, and math students. It is also suitable for practitioners working on data analytics projects. This book has been used for more than ten years in numerous courses at the Technical University of Munich, Germany, in short courses at several other universities, and in tutorials at scientific conferences. Much of the content is based on the results of industrial research and development projects at Siemens.
This book is a comprehensive introduction to the methods and algorithms and approaches of modern data analytics.
Author: Thomas A. Runkler
Publisher: Springer Science & Business Media
This book considers why institutional forms of modern capitalist economies differ internationally, and proposes a typology of capitalism based on the theory of institutional complementarity. Different economic models are not simply characterized by different institutional forms, but also by particular patterns of interaction between complementary institutions which are the core characteristics of these models. Institutions are not just simply devices which would be chosen by 'social engineers' in order to perform a function as efficiently as possible; they are the outcome of a political economy process. Therefore, institutional change should be envisaged not as a move towards a hypothetical 'one best way', but as a result of socio-political compromises. Based on a theory of institutions and comparative capitalism, the book proposes an analysis of the diversity of modern economies - from America to Korea - and identifies five different models: the market-based Anglo-Saxon model; Asian capitalism; the Continental European model; the social democratic economies; and the Mediterranean model. Each of these types of capitalism is characterized by specific institutional complementarities. The question of the stability of the Continental European model of capitalism has been open since the beginning of the 1990s: inferior macroeconomic performance compared to Anglo-Saxon economies, alleged unsustainability of its welfare systems, too rigid markets, etc. The book examines the institutional transformations that have taken place within Continental European economies and analyses the political project behind the attempts at transforming the Continental model. It argues that Continental European economies will most likely stay very different from the market-based economies, and caat political strategies promoting institutional change aiming at convergence with the Anglo-Saxon model are bound to meet considerable opposition.
Analysis. in. Chapter. 4. The database is formatted so that individuals are
represented by lines of a matrix, and the variables ... Principal-Components
Analysis1 All the cluster analyses performed here are based on principal-
components analysis. ... The Ward algorithm is used, consolidated by the 'mobile-
Author: Bruno Amable
Publisher: OUP Oxford
Category: Business & Economics
This book presents advances in high performance computing as well as advances accomplished using high performance computing. It contains a collection of papers presenting results achieved in the collaboration of scientists from computer science, mathematics, physics, and mechanical engineering. From science problems to mathematical algorithms and on to the effective implementation of these algorithms on massively parallel and cluster computers, the book presents state-of-the-art methods and technology, and exemplary results in these fields.
One of the most successful theories in modern science is statistical mechanics,
which allows us to understand the macroscopic (thermodynamic) properties of
matter from a statistical analysis of the microscopic (mechanical) behavior of the ...
Author: Karl Heinz Hoffmann
Publisher: Springer Science & Business Media
Proceedings of China Modern Logistics Engineering covers nearly all areas of logistics engineering technology, focusing on the latest findings and the following theoretical aspects: Logistics Systems and Management Research; Green Logistics and Emergency Logistics; Enterprise Logistics; Material Handling; Warehousing Technology Research; Supply Chain Management; Logistics Equipment; Logistics Packaging Technology; Third-party Logistics, etc. The book will help readers to grasp the relevant aspects of the theory involved, research and development trends, while also offering guidance for their work and related studies. It is intended for researchers, scholars and graduate students in logistics management, logistics engineering, transportation, business administration, E-commerce and industrial engineering.
After the many steps of repetitive computations, if the cluster center and the matrix
set meet the conditions of thresholds ... Besides, the algorithm will be simulation
analyzed using MATLAB software, and at last take the some regions such as ...
Author: Logistics Engineering Institution,
Category: Technology & Engineering