Data Classification And Incremental Clustering In Data Mining And Machine Learning

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Data Classification and Incremental Clustering in Data Mining and Machine Learning

Author : Sanjay Chakraborty
Publisher : Springer Nature
ISBN 13 : 3030930882
Total Pages : 210 pages
Book Rating : 4.0/5 (39 download)

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Book Synopsis Data Classification and Incremental Clustering in Data Mining and Machine Learning by : Sanjay Chakraborty

Download or read book Data Classification and Incremental Clustering in Data Mining and Machine Learning written by Sanjay Chakraborty and published by Springer Nature. This book was released on 2022-05-10 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a comprehensive, hands-on guide to the basics of data mining and machine learning with a special emphasis on supervised and unsupervised learning methods. The book lays stress on the new ways of thinking needed to master in machine learning based on the Python, R, and Java programming platforms. This book first provides an understanding of data mining, machine learning and their applications, giving special attention to classification and clustering techniques. The authors offer a discussion on data mining and machine learning techniques with case studies and examples. The book also describes the hands-on coding examples of some well-known supervised and unsupervised learning techniques using three different and popular coding platforms: R, Python, and Java. This book explains some of the most popular classification techniques (K-NN, Naïve Bayes, Decision tree, Random forest, Support vector machine etc,) along with the basic description of artificial neural network and deep neural network. The book is useful for professionals, students studying data mining and machine learning, and researchers in supervised and unsupervised learning techniques.

Data Mining In Time Series And Streaming Databases

Author : Mark Last
Publisher : World Scientific
ISBN 13 : 9813228059
Total Pages : 196 pages
Book Rating : 4.8/5 (132 download)

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Book Synopsis Data Mining In Time Series And Streaming Databases by : Mark Last

Download or read book Data Mining In Time Series And Streaming Databases written by Mark Last and published by World Scientific. This book was released on 2018-01-12 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt: This compendium is a completely revised version of an earlier book, Data Mining in Time Series Databases, by the same editors. It provides a unique collection of new articles written by leading experts that account for the latest developments in the field of time series and data stream mining.The emerging topics covered by the book include weightless neural modeling for mining data streams, using ensemble classifiers for imbalanced and evolving data streams, document stream mining with active learning, and many more. In particular, it addresses the domain of streaming data, which has recently become one of the emerging topics in Data Science, Big Data, and related areas. Existing titles do not provide sufficient information on this topic.

Data Clustering

Author : Charu C. Aggarwal
Publisher : CRC Press
ISBN 13 : 1315360411
Total Pages : 654 pages
Book Rating : 4.3/5 (153 download)

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Book Synopsis Data Clustering by : Charu C. Aggarwal

Download or read book Data Clustering written by Charu C. Aggarwal and published by CRC Press. This book was released on 2018-09-03 with total page 654 pages. Available in PDF, EPUB and Kindle. Book excerpt: Research on the problem of clustering tends to be fragmented across the pattern recognition, database, data mining, and machine learning communities. Addressing this problem in a unified way, Data Clustering: Algorithms and Applications provides complete coverage of the entire area of clustering, from basic methods to more refined and complex data clustering approaches. It pays special attention to recent issues in graphs, social networks, and other domains. The book focuses on three primary aspects of data clustering: Methods, describing key techniques commonly used for clustering, such as feature selection, agglomerative clustering, partitional clustering, density-based clustering, probabilistic clustering, grid-based clustering, spectral clustering, and nonnegative matrix factorization Domains, covering methods used for different domains of data, such as categorical data, text data, multimedia data, graph data, biological data, stream data, uncertain data, time series clustering, high-dimensional clustering, and big data Variations and Insights, discussing important variations of the clustering process, such as semisupervised clustering, interactive clustering, multiview clustering, cluster ensembles, and cluster validation In this book, top researchers from around the world explore the characteristics of clustering problems in a variety of application areas. They also explain how to glean detailed insight from the clustering process—including how to verify the quality of the underlying clusters—through supervision, human intervention, or the automated generation of alternative clusters.

Handbook of Research on Big Data Clustering and Machine Learning

Author : Garcia Marquez, Fausto Pedro
Publisher : IGI Global
ISBN 13 : 1799801071
Total Pages : 478 pages
Book Rating : 4.7/5 (998 download)

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Book Synopsis Handbook of Research on Big Data Clustering and Machine Learning by : Garcia Marquez, Fausto Pedro

Download or read book Handbook of Research on Big Data Clustering and Machine Learning written by Garcia Marquez, Fausto Pedro and published by IGI Global. This book was released on 2019-10-04 with total page 478 pages. Available in PDF, EPUB and Kindle. Book excerpt: As organizations continue to develop, there is an increasing need for technological methods that can keep up with the rising amount of data and information that is being generated. Machine learning is a tool that has become powerful due to its ability to analyze large amounts of data quickly. Machine learning is one of many technological advancements that is being implemented into a multitude of specialized fields. An extensive study on the execution of these advancements within professional industries is necessary. The Handbook of Research on Big Data Clustering and Machine Learning is an essential reference source that synthesizes the analytic principles of clustering and machine learning to big data and provides an interface between the main disciplines of engineering/technology and the organizational, administrative, and planning abilities of management. Featuring research on topics such as project management, contextual data modeling, and business information systems, this book is ideally designed for engineers, economists, finance officers, marketers, decision makers, business professionals, industry practitioners, academicians, students, and researchers seeking coverage on the implementation of big data and machine learning within specific professional fields.

Data Mining and Knowledge Discovery Handbook

Author : Oded Maimon
Publisher : Springer Science & Business Media
ISBN 13 : 038725465X
Total Pages : 1378 pages
Book Rating : 4.3/5 (872 download)

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Book Synopsis Data Mining and Knowledge Discovery Handbook by : Oded Maimon

Download or read book Data Mining and Knowledge Discovery Handbook written by Oded Maimon and published by Springer Science & Business Media. This book was released on 2006-05-28 with total page 1378 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Mining and Knowledge Discovery Handbook organizes all major concepts, theories, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery in databases (KDD) into a coherent and unified repository. This book first surveys, then provides comprehensive yet concise algorithmic descriptions of methods, including classic methods plus the extensions and novel methods developed recently. This volume concludes with in-depth descriptions of data mining applications in various interdisciplinary industries including finance, marketing, medicine, biology, engineering, telecommunications, software, and security. Data Mining and Knowledge Discovery Handbook is designed for research scientists and graduate-level students in computer science and engineering. This book is also suitable for professionals in fields such as computing applications, information systems management, and strategic research management.

Classification, Clustering, and Data Mining Applications

Author : David Banks
Publisher : Springer Science & Business Media
ISBN 13 : 3642171036
Total Pages : 642 pages
Book Rating : 4.6/5 (421 download)

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Book Synopsis Classification, Clustering, and Data Mining Applications by : David Banks

Download or read book Classification, Clustering, and Data Mining Applications written by David Banks and published by Springer Science & Business Media. This book was released on 2011-01-07 with total page 642 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume describes new methods with special emphasis on classification and cluster analysis. These methods are applied to problems in information retrieval, phylogeny, medical diagnosis, microarrays, and other active research areas.

BIG DATA ANALYTICS

Author : Parag Kulkarni
Publisher : PHI Learning Pvt. Ltd.
ISBN 13 : 8120351169
Total Pages : 206 pages
Book Rating : 4.1/5 (23 download)

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Book Synopsis BIG DATA ANALYTICS by : Parag Kulkarni

Download or read book BIG DATA ANALYTICS written by Parag Kulkarni and published by PHI Learning Pvt. Ltd.. This book was released on 2016-07-07 with total page 206 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book is an unstructured data mining quest, which takes the reader through different features of unstructured data mining while unfolding the practical facets of Big Data. It emphasizes more on machine learning and mining methods required for processing and decision-making. The text begins with the introduction to the subject and explores the concept of data mining methods and models along with the applications. It then goes into detail on other aspects of Big Data analytics, such as clustering, incremental learning, multi-label association and knowledge representation. The readers are also made familiar with business analytics to create value. The book finally ends with a discussion on the areas where research can be explored.

Data Classification

Author : Charu C. Aggarwal
Publisher : CRC Press
ISBN 13 : 1466586745
Total Pages : 710 pages
Book Rating : 4.4/5 (665 download)

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Book Synopsis Data Classification by : Charu C. Aggarwal

Download or read book Data Classification written by Charu C. Aggarwal and published by CRC Press. This book was released on 2014-07-25 with total page 710 pages. Available in PDF, EPUB and Kindle. Book excerpt: Comprehensive Coverage of the Entire Area of Classification Research on the problem of classification tends to be fragmented across such areas as pattern recognition, database, data mining, and machine learning. Addressing the work of these different communities in a unified way, Data Classification: Algorithms and Applications explores the underlying algorithms of classification as well as applications of classification in a variety of problem domains, including text, multimedia, social network, and biological data. This comprehensive book focuses on three primary aspects of data classification: Methods-The book first describes common techniques used for classification, including probabilistic methods, decision trees, rule-based methods, instance-based methods, support vector machine methods, and neural networks. Domains-The book then examines specific methods used for data domains such as multimedia, text, time-series, network, discrete sequence, and uncertain data. It also covers large data sets and data streams due to the recent importance of the big data paradigm. Variations-The book concludes with insight on variations of the classification process. It discusses ensembles, rare-class learning, distance function learning, active learning, visual learning, transfer learning, and semi-supervised learning as well as evaluation aspects of classifiers.

Classification, Clustering, and Data Analysis

Author : Krzystof Jajuga
Publisher : Springer Science & Business Media
ISBN 13 : 3642561810
Total Pages : 468 pages
Book Rating : 4.6/5 (425 download)

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Book Synopsis Classification, Clustering, and Data Analysis by : Krzystof Jajuga

Download or read book Classification, Clustering, and Data Analysis written by Krzystof Jajuga and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 468 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book presents a long list of useful methods for classification, clustering and data analysis. By combining theoretical aspects with practical problems, it is designed for researchers as well as for applied statisticians and will support the fast transfer of new methodological advances to a wide range of applications.

Applied Data Mining

Author : Guandong Xu
Publisher : CRC Press
ISBN 13 : 1466585846
Total Pages : 284 pages
Book Rating : 4.4/5 (665 download)

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Book Synopsis Applied Data Mining by : Guandong Xu

Download or read book Applied Data Mining written by Guandong Xu and published by CRC Press. This book was released on 2013-06-17 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data mining has witnessed substantial advances in recent decades. New research questions and practical challenges have arisen from emerging areas and applications within the various fields closely related to human daily life, e.g. social media and social networking. This book aims to bridge the gap between traditional data mining and the latest adv

Grouping Multidimensional Data

Author : Jacob Kogan
Publisher : Springer Science & Business Media
ISBN 13 : 3540283498
Total Pages : 273 pages
Book Rating : 4.5/5 (42 download)

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Book Synopsis Grouping Multidimensional Data by : Jacob Kogan

Download or read book Grouping Multidimensional Data written by Jacob Kogan and published by Springer Science & Business Media. This book was released on 2006-02-08 with total page 273 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Data Mining Patterns: New Methods and Applications

Author : Poncelet, Pascal
Publisher : IGI Global
ISBN 13 : 1599041642
Total Pages : 324 pages
Book Rating : 4.5/5 (99 download)

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Book Synopsis Data Mining Patterns: New Methods and Applications by : Poncelet, Pascal

Download or read book Data Mining Patterns: New Methods and Applications written by Poncelet, Pascal and published by IGI Global. This book was released on 2007-08-31 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book provides an overall view of recent solutions for mining, and explores new patterns,offering theoretical frameworks and presenting challenges and possible solutions concerning pattern extractions, emphasizing research techniques and real-world applications. It portrays research applications in data models, methodologies for mining patterns, multi-relational and multidimensional pattern mining, fuzzy data mining, data streaming and incremental mining"--Provided by publisher.

Modern Technologies for Big Data Classification and Clustering

Author : Seetha, Hari
Publisher : IGI Global
ISBN 13 : 1522528067
Total Pages : 381 pages
Book Rating : 4.5/5 (225 download)

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Book Synopsis Modern Technologies for Big Data Classification and Clustering by : Seetha, Hari

Download or read book Modern Technologies for Big Data Classification and Clustering written by Seetha, Hari and published by IGI Global. This book was released on 2017-07-12 with total page 381 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Advances in Knowledge Discovery and Data Mining

Author : Ming-Syan Cheng
Publisher : Springer
ISBN 13 : 3540478876
Total Pages : 582 pages
Book Rating : 4.5/5 (44 download)

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Book Synopsis Advances in Knowledge Discovery and Data Mining by : Ming-Syan Cheng

Download or read book Advances in Knowledge Discovery and Data Mining written by Ming-Syan Cheng and published by Springer. This book was released on 2003-08-01 with total page 582 pages. Available in PDF, EPUB and Kindle. Book excerpt: Knowledge discovery and data mining have become areas of growing significance because of the recent increasing demand for KDD techniques, including those used in machine learning, databases, statistics, knowledge acquisition, data visualization, and high performance computing. In view of this, and following the success of the five previous PAKDD conferences, the sixth Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2002) aimed to provide a forum for the sharing of original research results, innovative ideas, state-of-the-art developments, and implementation experiences in knowledge discovery and data mining among researchers in academic and industrial organizations. Much work went into preparing a program of high quality. We received 128 submissions. Every paper was reviewed by 3 program committee members, and 32 were selected as regular papers and 20 were selected as short papers, representing a 25% acceptance rate for regular papers. The PAKDD 2002 program was further enhanced by two keynote speeches, delivered by Vipin Kumar from the Univ. of Minnesota and Rajeev Rastogi from AT&T. In addition, PAKDD 2002 was complemented by three tutorials, XML and data mining (by Kyuseok Shim and Surajit Chadhuri), mining customer data across various customer touchpoints at- commerce sites (by Jaideep Srivastava), and data clustering analysis, from simple groupings to scalable clustering with constraints (by Osmar Zaiane and Andrew Foss).

Data Mining in Time Series Databases

Author : Mark Last
Publisher : World Scientific Publishing Company Incorporated
ISBN 13 : 9789812382900
Total Pages : 192 pages
Book Rating : 4.3/5 (829 download)

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Book Synopsis Data Mining in Time Series Databases by : Mark Last

Download or read book Data Mining in Time Series Databases written by Mark Last and published by World Scientific Publishing Company Incorporated. This book was released on 2004 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: Adding the time dimension to real-world databases produces Time Series Databases (TSDB) and introduces new aspects and difficulties to data mining and knowledge discovery. This book covers the state-of-the-art methodology for mining time series databases. The novel data mining methods presented in the book include techniques for efficient segmentation, indexing, and classification of noisy and dynamic time series. A graph-based method for anomaly detection in time series is described and the book also studies the implications of a novel and potentially useful representation of time series as strings. The problem of detecting changes in data mining models that are induced from temporal databases is additionally discussed. Contents: A Survey of Recent Methods for Efficient Retrieval of Similar Time Sequences (H M Lie); Indexing of Compressed Time Series (E Fink & K Pratt); Boosting Interval-Based Literal: Variable Length and Early Classification (J J Rodriguez Diez); Segmenting Time Series: A Survey and Novel Approach (E Keogh et al.); Indexing Similar Time Series under Conditions of Noise (M Vlachos et al.); Classification of Events in Time Series of Graphs (H Bunke & M Kraetzl); Median Strings--A Review (X Jiang et al.); Change Detection in Classfication Models of Data Mining (G Zeira et al.). Readership: Graduate students, reseachers and practitioners in the fields of data mining, machine learning, databases and statistics.

Data Mining with Decision Trees

Author : Lior Rokach
Publisher : World Scientific
ISBN 13 : 9812771727
Total Pages : 263 pages
Book Rating : 4.8/5 (127 download)

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Book Synopsis Data Mining with Decision Trees by : Lior Rokach

Download or read book Data Mining with Decision Trees written by Lior Rokach and published by World Scientific. This book was released on 2008 with total page 263 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the first comprehensive book dedicated entirely to the field of decision trees in data mining and covers all aspects of this important technique. Decision trees have become one of the most powerful and popular approaches in knowledge discovery and data mining, the science and technology of exploring large and complex bodies of data in order to discover useful patterns. The area is of great importance because it enables modeling and knowledge extraction from the abundance of data available. Both theoreticians and practitioners are continually seeking techniques to make the process more efficient, cost-effective and accurate. Decision trees, originally implemented in decision theory and statistics, are highly effective tools in other areas such as data mining, text mining, information extraction, machine learning, and pattern recognition. This book invites readers to explore the many benefits in data mining that decision trees offer:: Self-explanatory and easy to follow when compacted; Able to handle a variety of input data: nominal, numeric and textual; Able to process datasets that may have errors or missing values; High predictive performance for a relatively small computational effort; Available in many data mining packages over a variety of platforms; Useful for various tasks, such as classification, regression, clustering and feature selection . Sample Chapter(s). Chapter 1: Introduction to Decision Trees (245 KB). Chapter 6: Advanced Decision Trees (409 KB). Chapter 10: Fuzzy Decision Trees (220 KB). Contents: Introduction to Decision Trees; Growing Decision Trees; Evaluation of Classification Trees; Splitting Criteria; Pruning Trees; Advanced Decision Trees; Decision Forests; Incremental Learning of Decision Trees; Feature Selection; Fuzzy Decision Trees; Hybridization of Decision Trees with Other Techniques; Sequence Classification Using Decision Trees. Readership: Researchers, graduate and undergraduate students in information systems, engineering, computer science, statistics and management.

Supervised and Unsupervised Learning for Data Science

Author : Michael W. Berry
Publisher : Springer Nature
ISBN 13 : 3030224759
Total Pages : 191 pages
Book Rating : 4.0/5 (32 download)

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Book Synopsis Supervised and Unsupervised Learning for Data Science by : Michael W. Berry

Download or read book Supervised and Unsupervised Learning for Data Science written by Michael W. Berry and published by Springer Nature. This book was released on 2019-09-04 with total page 191 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers the state of the art in learning algorithms with an inclusion of semi-supervised methods to provide a broad scope of clustering and classification solutions for big data applications. Case studies and best practices are included along with theoretical models of learning for a comprehensive reference to the field. The book is organized into eight chapters that cover the following topics: discretization, feature extraction and selection, classification, clustering, topic modeling, graph analysis and applications. Practitioners and graduate students can use the volume as an important reference for their current and future research and faculty will find the volume useful for assignments in presenting current approaches to unsupervised and semi-supervised learning in graduate-level seminar courses. The book is based on selected, expanded papers from the Fourth International Conference on Soft Computing in Data Science (2018). Includes new advances in clustering and classification using semi-supervised and unsupervised learning; Address new challenges arising in feature extraction and selection using semi-supervised and unsupervised learning; Features applications from healthcare, engineering, and text/social media mining that exploit techniques from semi-supervised and unsupervised learning.