Clustering For Data Mining
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Classification Clustering And Data Mining Applications
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Author : David Banks
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-01-07
Classification Clustering And Data Mining Applications written by David Banks and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-01-07 with Language Arts & Disciplines categories.
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.
Data Clustering
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Author : Charu C. Aggarwal
language : en
Publisher: CRC Press
Release Date : 2018-09-03
Data Clustering written by Charu C. Aggarwal and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-03 with Business & Economics categories.
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.
Clustering For Data Mining
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Author : Boris Mirkin
language : en
Publisher: CRC Press
Release Date : 2005-04-29
Clustering For Data Mining written by Boris Mirkin and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-04-29 with Business & Economics categories.
Often considered more as an art than a science, the field of clustering has been dominated by learning through examples and by techniques chosen almost through trial-and-error. Even the most popular clustering methods--K-Means for partitioning the data set and Ward's method for hierarchical clustering--have lacked the theoretical attention that wou
Grouping Multidimensional Data
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Author : Jacob Kogan
language : en
Publisher: Taylor & Francis
Release Date : 2006-02-10
Grouping Multidimensional Data written by Jacob Kogan and has been published by Taylor & Francis this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-02-10 with Computers categories.
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Data Classification And Incremental Clustering In Data Mining And Machine Learning
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Author : Sanjay Chakraborty
language : en
Publisher: Springer Nature
Release Date : 2022-05-10
Data Classification And Incremental Clustering In Data Mining And Machine Learning written by Sanjay Chakraborty and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-10 with Technology & Engineering categories.
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.
Advances In K Means Clustering
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Author : Junjie Wu
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-07-09
Advances In K Means Clustering written by Junjie Wu and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-07-09 with Computers categories.
Nearly everyone knows K-means algorithm in the fields of data mining and business intelligence. But the ever-emerging data with extremely complicated characteristics bring new challenges to this "old" algorithm. This book addresses these challenges and makes novel contributions in establishing theoretical frameworks for K-means distances and K-means based consensus clustering, identifying the "dangerous" uniform effect and zero-value dilemma of K-means, adapting right measures for cluster validity, and integrating K-means with SVMs for rare class analysis. This book not only enriches the clustering and optimization theories, but also provides good guidance for the practical use of K-means, especially for important tasks such as network intrusion detection and credit fraud prediction. The thesis on which this book is based has won the "2010 National Excellent Doctoral Dissertation Award", the highest honor for not more than 100 PhD theses per year in China.
Cluster Analysis For Data Mining And System Identification
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Author : János Abonyi
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-06-22
Cluster Analysis For Data Mining And System Identification written by János Abonyi and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-06-22 with Mathematics categories.
The aim of this book is to illustrate that advanced fuzzy clustering algorithms can be used not only for partitioning of the data. It can also be used for visualization, regression, classification and time-series analysis, hence fuzzy cluster analysis is a good approach to solve complex data mining and system identification problems. This book is oriented to undergraduate and postgraduate and is well suited for teaching purposes.
Clustering For Data Mining
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Author : Boris Mirkin
language : en
Publisher: Chapman and Hall/CRC
Release Date : 2005-04-29
Clustering For Data Mining written by Boris Mirkin and has been published by Chapman and Hall/CRC this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-04-29 with Business & Economics categories.
Often considered more as an art than a science, the field of clustering has been dominated by learning through examples and by techniques chosen almost through trial-and-error. Even the most popular clustering methods--K-Means for partitioning the data set and Ward's method for hierarchical clustering--have lacked the theoretical attention that would establish a firm relationship between the two methods and relevant interpretation aids. Rather than the traditional set of ad hoc techniques, Clustering for Data Mining: A Data Recovery Approach presents a theory that not only closes gaps in K-Means and Ward methods, but also extends them into areas of current interest, such as clustering mixed scale data and incomplete clustering. The author suggests original methods for both cluster finding and cluster description, addresses related topics such as principal component analysis, contingency measures, and data visualization, and includes nearly 60 computational examples covering all stages of clustering, from data pre-processing to cluster validation and results interpretation. This author's unique attention to data recovery methods, theory-based advice, pre- and post-processing issues that are beyond the scope of most texts, and clear, practical instructions for real-world data mining make this book ideally suited for virtually all purposes: for teaching, for self-study, and for professional reference.
Fuzzy C Mean Clustering Using Data Mining
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Author : VIGNESH RAMAMOORTHY H
language : en
Publisher: BookRix
Release Date : 2019-11-28
Fuzzy C Mean Clustering Using Data Mining written by VIGNESH RAMAMOORTHY H and has been published by BookRix this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-28 with Technology & Engineering categories.
The goal of traditional clustering is to assign each data point to one and only one cluster. In contrast, fuzzy clustering assigns different degrees of membership to each point. The membership of a point is thus shared among various clusters. This creates the concept of fuzzy boundaries which differs from the traditional concept of well-defined boundaries. In hard clustering, data is divided into distinct clusters, where each data element belongs to exactly one cluster. In fuzzy clustering (also referred to as soft clustering), data elements can belong to more than one cluster, and associated with each element is a set of membership levels. These indicate the strength of the association between that data element and a particular cluster. Fuzzy clustering is a process of assigning these membership levels, and then using them to assign data elements to one or more clusters. This algorithm uses the FCM traditional algorithm to locate the centers of clusters for a bulk of data points. The potential of all data points is being calculated with respect to specified centers. The availability of dividing the data set into large number of clusters will slow the processing time and needs more memory size for the program. Hence traditional clustering should device the data to four clusters and each data point should be located in one specified cluster .Imprecision in data and information gathered from and about our environment is either statistical(e.g., the outcome of a coin toss is a matter of chance) or no statistical (e.g., “apply the brakes pretty soon”). Many algorithms can be implemented to develop clustering of data sets. Fuzzy C-mean clustering (FCM) is efficient and common algorithm. We are tuning this algorithm to get a solution for the rest of data point which omitted because of its farness from all clusters. To develop a high performance algorithm that sort and group data set in variable number of clusters to use this data in control and managing of those clusters.
Data Science
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Author : Francesco Palumbo
language : en
Publisher: Springer
Release Date : 2017-07-04
Data Science written by Francesco Palumbo and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-07-04 with Mathematics categories.
This edited volume on the latest advances in data science covers a wide range of topics in the context of data analysis and classification. In particular, it includes contributions on classification methods for high-dimensional data, clustering methods, multivariate statistical methods, and various applications. The book gathers a selection of peer-reviewed contributions presented at the Fifteenth Conference of the International Federation of Classification Societies (IFCS2015), which was hosted by the Alma Mater Studiorum, University of Bologna, from July 5 to 8, 2015.