Download or read book Nonsmooth Optimization Models and Algorithms for Data Clustering and Visualization written by Ehsan Mohebi and published by . This book was released on 2014 with total page 326 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Cluster analysis deals with the problem of organization of a collection of patterns into clusters based on a similarity measure. Various distance functions can be used to define this measure. Clustering problems with the similarity measure defined by the squared Euclidean distance have been studied extensively over the last five decades. However, problems with other Minkowski norms have attracted significantly less attention. The use of different similarity measures may help to identify different cluster structures of a data set. This in turn may help to significantly improve the decision making process. High dimensional data visualization is another important task in the field of data mining and pattern recognition. To date, the principal component analysis and the self-organizing maps techniques have been used to solve such problems. In this thesis we develop algorithms for solving clustering problems in large data sets using various similarity measures. Such similarity measures are based on the squared L2 as well as L1 and L {infinity symbol} norms. In all cases the clustering problem is a global optimization problem with nonsmooth nonconvex objective functions. In many datasets these problems are large scale and the conventional global optimization algorithms are not efficient for solving such problems. Therefore we propose to apply local search methods for solving clustering problems, however the success of these methods strongly depends on the choice of starting cluster centers. To deal with the nonconvexity of the clustering problems we propose incremental algorithms for their solution which helps us to design a special procedure to generate starting points for cluster centers. Such an approach allows one to find global or near global solutions to the clustering problem. In order to solve nonsmooth clustering problems we apply both efficient nonsmooth optimization algorithms as well as smoothing techniques. To test the proposed algorithms we apply them to solve clustering problems in small, medium size and large data sets. Furthermore, these algorithms are compared with many other clustering algorithms using results of numerical experiments. The Self Organizing Maps (SOM) is one of the topology visualizing tool that contains a set of neurons that gradually adapt to input data space by competitive learning and form clusters. The topology preservation of the SOM strongly depends on the learning process. Due to this limitation one cannot guarantee the convergence of the SOM in data sets with clusters of arbitrary shape. Therefore it is important to develop more accurate data visualization and clustering algorithms. In this thesis, Constrained SOM (CSOM) is proposed as the new version of the SOM by modifying the learning algorithm. The idea is to introduce an adaptive constraint parameter to the learning process to improve the topology preservation and mapping quality of the basic SOM. The computational complexity of the CSOM is less than that of the SOM. Mapping quality of the SOM is sensitive to the map topology and initialization of neurons. Thus in this research, a modified version of the SOM (MSOM) is proposed to improve the convergence of the SOM. An initialization algorithm based on split and merge of clusters is introduced to initialize neurons of the SOM. The initialization algorithm speeds up the learning process in large high dimensional data sets. A topology based on this initialization is developed to minimize the vector quantization error and topology preservation of the self organizing maps. The CSOM and MSOM algorithms are tested on small to large size real-world datasets. Finally, a convolutional structure of the Recursive Modified SOM is proposed to cope with the diversity of styles and shapes in digits recognition. The proposed recursive structure can learn various behaviors of incoming images. The numerical results on the well-known MNIST dataset demonstrate the superiority of the proposed algorithm over existing SOM-based approaches." -- Abstract.
Download or read book Partitional Clustering via Nonsmooth Optimization written by Adil M. Bagirov and published by Springer Nature. This book was released on 2020-02-24 with total page 343 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes optimization models of clustering problems and clustering algorithms based on optimization techniques, including their implementation, evaluation, and applications. The book gives a comprehensive and detailed description of optimization approaches for solving clustering problems; the authors' emphasis on clustering algorithms is based on deterministic methods of optimization. The book also includes results on real-time clustering algorithms based on optimization techniques, addresses implementation issues of these clustering algorithms, and discusses new challenges arising from big data. The book is ideal for anyone teaching or learning clustering algorithms. It provides an accessible introduction to the field and it is well suited for practitioners already familiar with the basics of optimization.
Download or read book Graph Based Clustering and Data Visualization Algorithms written by Ágnes Vathy-Fogarassy and published by Springer Science & Business Media. This book was released on 2013-05-24 with total page 120 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work presents a data visualization technique that combines graph-based topology representation and dimensionality reduction methods to visualize the intrinsic data structure in a low-dimensional vector space. The application of graphs in clustering and visualization has several advantages. A graph of important edges (where edges characterize relations and weights represent similarities or distances) provides a compact representation of the entire complex data set. This text describes clustering and visualization methods that are able to utilize information hidden in these graphs, based on the synergistic combination of clustering, graph-theory, neural networks, data visualization, dimensionality reduction, fuzzy methods, and topology learning. The work contains numerous examples to aid in the understanding and implementation of the proposed algorithms, supported by a MATLAB toolbox available at an associated website.
Download or read book Partitional Clustering Algorithms written by M. Emre Celebi and published by Springer. This book was released on 2014-11-07 with total page 420 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on partitional clustering algorithms, which are commonly used in engineering and computer scientific applications. The goal of this volume is to summarize the state-of-the-art in partitional clustering. The book includes such topics as center-based clustering, competitive learning clustering and density-based clustering. Each chapter is contributed by a leading expert in the field.
Download or read book Data Clustering written by Guojun Gan and published by SIAM. This book was released on 2007-07-12 with total page 471 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reference and compendium of algorithms for pattern recognition, data mining and statistical computing.
Download or read book Nonsmooth Optimization Algorithms for Clusterwise Linear Regression written by Hijran Mirzayeva and published by . This book was released on 2013 with total page 214 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data mining is about solving problems by analyzing data that present in databases. Supervised and unsupervised data classification (clustering) are among the most important techniques in data mining. Regression analysis is the process of fitting a function (often linear) to the data to discover how one or more variables vary as a function of another. The aim of clusterwise regression is to combine both of these techniques, to discover trends within data, when more than one trend is likely to exist. Clusterwise regresssion has applications for instance in market segmentation, where it allows one to gather information on customer behaviors for several unknown groups of customers. There exist different methods for solving clusterwise linear regression problems. In spite of that, the development of efficient algorithms for solving clusterwise linear regression problems is still an important research topic. In this thesis our aim is to develop new algorithms for solving clusterwise linear regression problems in large data sets based on incremental and nonsmooth opimization approaches. Three new methods for solving clusterwise linear regression problems are developed and numerically tested on publicly available data sets for regression analysis. -- Taken from abstract.
Download or read book Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization written by B.K. Tripathy and published by CRC Press. This book was released on 2021-09-01 with total page 174 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization describes such algorithms as Locally Linear Embedding (LLE), Laplacian Eigenmaps, Isomap, Semidefinite Embedding, and t-SNE to resolve the problem of dimensionality reduction in the case of non-linear relationships within the data. Underlying mathematical concepts, derivations, and proofs with logical explanations for these algorithms are discussed, including strengths and limitations. The book highlights important use cases of these algorithms and provides examples along with visualizations. Comparative study of the algorithms is presented to give a clear idea on selecting the best suitable algorithm for a given dataset for efficient dimensionality reduction and data visualization. FEATURES Demonstrates how unsupervised learning approaches can be used for dimensionality reduction Neatly explains algorithms with a focus on the fundamentals and underlying mathematical concepts Describes the comparative study of the algorithms and discusses when and where each algorithm is best suitable for use Provides use cases, illustrative examples, and visualizations of each algorithm Helps visualize and create compact representations of high dimensional and intricate data for various real-world applications and data analysis This book is aimed at professionals, graduate students, and researchers in Computer Science and Engineering, Data Science, Machine Learning, Computer Vision, Data Mining, Deep Learning, Sensor Data Filtering, Feature Extraction for Control Systems, and Medical Instruments Input Extraction.
Download or read book Metaheuristics for Data Clustering and Image Segmentation written by Meera Ramadas and published by Springer. This book was released on 2018-12-12 with total page 167 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book, differential evolution and its modified variants are applied to the clustering of data and images. Metaheuristics have emerged as potential algorithms for dealing with complex optimization problems, which are otherwise difficult to solve using traditional methods. In this regard, differential evolution is considered to be a highly promising technique for optimization and is being used to solve various real-time problems. The book studies the algorithms in detail, tests them on a range of test images, and carefully analyzes their performance. Accordingly, it offers a valuable reference guide for all researchers, students and practitioners working in the fields of artificial intelligence, optimization and data analytics.
Download or read book Encyclopedia of Optimization written by Christodoulos A. Floudas and published by Springer Science & Business Media. This book was released on 2008-09-04 with total page 4646 pages. Available in PDF, EPUB and Kindle. Book excerpt: The goal of the Encyclopedia of Optimization is to introduce the reader to a complete set of topics that show the spectrum of research, the richness of ideas, and the breadth of applications that has come from this field. The second edition builds on the success of the former edition with more than 150 completely new entries, designed to ensure that the reference addresses recent areas where optimization theories and techniques have advanced. Particularly heavy attention resulted in health science and transportation, with entries such as "Algorithms for Genomics", "Optimization and Radiotherapy Treatment Design", and "Crew Scheduling".
Download or read book Graph Based Clustering and Data Visualization Algorithms written by Ágnes Vathy-Fogarassy and published by . This book was released on 2013-06-30 with total page 126 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Clustering Methods for Big Data Analytics written by Olfa Nasraoui and published by Springer. This book was released on 2018-10-27 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book highlights the state of the art and recent advances in Big Data clustering methods and their innovative applications in contemporary AI-driven systems. The book chapters discuss Deep Learning for Clustering, Blockchain data clustering, Cybersecurity applications such as insider threat detection, scalable distributed clustering methods for massive volumes of data; clustering Big Data Streams such as streams generated by the confluence of Internet of Things, digital and mobile health, human-robot interaction, and social networks; Spark-based Big Data clustering using Particle Swarm Optimization; and Tensor-based clustering for Web graphs, sensor streams, and social networks. The chapters in the book include a balanced coverage of big data clustering theory, methods, tools, frameworks, applications, representation, visualization, and clustering validation.
Download or read book Projection Based Clustering through Self Organization and Swarm Intelligence written by Michael Christoph Thrun and published by Springer Vieweg. This book was released on 2018-01-22 with total page 201 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is published open access under a CC BY 4.0 license. It covers aspects of unsupervised machine learning used for knowledge discovery in data science and introduces a data-driven approach to cluster analysis, the Databionic swarm (DBS). DBS consists of the 3D landscape visualization and clustering of data. The 3D landscape enables 3D printing of high-dimensional data structures. The clustering and number of clusters or an absence of cluster structure are verified by the 3D landscape at a glance. DBS is the first swarm-based technique that shows emergent properties while exploiting concepts of swarm intelligence, self-organization and the Nash equilibrium concept from game theory. It results in the elimination of a global objective function and the setting of parameters. By downloading the R package DBS can be applied to data drawn from diverse research fields and used even by non-professionals in the field of data mining.
Download or read book Nature Inspired Optimization Algorithms Based Hybrid Clustering Mechanisms written by Jaya Mabel Rani A and published by Mohammed Abdul Sattar. This book was released on 2024-01-10 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Today data has grown more and more all around the world in tremendous way. According to Statista, the total amount of data has grown has forecasted globally by 2021 as 79 zettabytes. Using this data, data analyst can analyse, visualize and construct the pattern based on end users requirements. For analyzing and visualization the data, here in need of more fundamental techniques for understanding types of data sets, size and frequency of data set to take proper decision. There are different types of data such as relational data base, could be data warehouse database, transactional data, multimedia data, spatial data, WWW data, time series data, heterogeneous data, text data. There are more and more number of data mining techniques including pattern recognition, and machine learning algorithms. This book focused on data clustering technique, which is one of the sub part of machine learning. Clustering is one of the Unsupervised Machine Learning technique used for statistical data analysis in many fields, which is one of the sub branch of data mining. There are two main sub branches such as supervised machine learning and unsupervised machine learning under data mining. All classification methods including Rule based classification, Decision Tree (DT) classification, Random forest classification, support vector machine, etc., and linear regression based learning are come under Supervised Learning. Then all clustering algorithms such as K-Means (KM), K-Harmonic Means (KHM), Fuzzy clustering, Hybrid clustering, Optimization based clustering association based mining etc., are come under unsupervised clustering. Clustering algorithms can also be categorized into different types such as, traditional clustering algorithms such as, hierarchical clustering algorithms, grid based clustering, partitioning-based clustering, density based clustering. There are wide variety of clustering algorithms to cluster the data point into a set of disjoint classes. After clustering of the data all related data objects come under one group of data and different or dissimilar data objects come under another cluster of data. Clustering algorithms can be applied in most of the fields such as medical, engineering, financial forecasting, education, business, commerce, and so on. Clustering Algorithms can also use in Data Science to analyse more complicated problems and to get more valuable insights from the data.
Download or read book A Novel Non smooth Optimization Algorithm for Clustering Problems written by Parvaneh Shabanzadeh and published by . This book was released on 2009 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Scientific and Technical Aerospace Reports written by and published by . This book was released on 1995 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt: Lists citations with abstracts for aerospace related reports obtained from world wide sources and announces documents that have recently been entered into the NASA Scientific and Technical Information Database.
Download or read book Data Clustering and Image Segmentation Through Genetic Algorithms written by Sujata Dash and published by Engineering Science Reference. This book was released on 2018-08-03 with total page 160 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book provides a broad overview of genetic algorithms, clustering algorithms influenced by genetic algorithms, improvements attained in the field of image segmentation and their application by using genetic algorithms. It also explores the comparative analysis of earlier methods and the recent ones proposed with the use of genetic algorithms"--
Download or read book Projection Based Clustering Through Self Organization and Swarm Intelligence written by Michael Christoph Thrun and published by . This book was released on 2020-10-08 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers aspects of unsupervised machine learning used for knowledge discovery in data science and introduces a data-driven approach to cluster analysis, the Databionic swarm (DBS). DBS consists of the 3D landscape visualization and clustering of data. The 3D landscape enables 3D printing of high-dimensional data structures.The clustering and number of clusters or an absence of cluster structure are verified by the 3D landscape at a glance. DBS is the first swarm-based technique that shows emergent properties while exploiting concepts of swarm intelligence, self-organization and the Nash equilibrium concept from game theory. It results in the elimination of a global objective function and the setting of parameters. By downloading the R package DBS can be applied to data drawn from diverse research fields and used even by non-professionals in the field of data mining. This work was published by Saint Philip Street Press pursuant to a Creative Commons license permitting commercial use. All rights not granted by the work's license are retained by the author or authors.
