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Titlebook: Core Data Analysis: Summarization, Correlation, and Visualization; Boris Mirkin Textbook 2019Latest edition Springer Nature Switzerland AG

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書目名稱Core Data Analysis: Summarization, Correlation, and Visualization
編輯Boris Mirkin
視頻videohttp://file.papertrans.cn/239/238235/238235.mp4
概述Focuses on the encoder-decoder interpretation of summarization methods, such as Principal Component Analysis and K-means clustering.Supplies an in-depth description of K-means partitioning including a
叢書名稱Undergraduate Topics in Computer Science
圖書封面Titlebook: Core Data Analysis: Summarization, Correlation, and Visualization; Boris Mirkin Textbook 2019Latest edition Springer Nature Switzerland AG
描述.This text examines the goals of data analysis with respect to enhancing knowledge, and identifies data summarization and correlation analysis as the core issues. Data summarization, both quantitative and categorical, is treated within the encoder-decoder paradigm bringing forward a number of mathematically supported insights into the methods and relations between them. Two Chapters describe methods for categorical summarization: partitioning, divisive clustering and separate cluster finding and another explain the methods for quantitative summarization, Principal Component Analysis and PageRank. .Features:.·??????? An in-depth presentation of K-means partitioning including a corresponding Pythagorean decomposition of the data scatter. .·??????? Advice regarding such issues as clustering of categorical and mixed scale data, similarity and network data, interpretation aids, anomalous clusters, the number of clusters, etc..·??????? Thorough attention to data-driven modelling including a number of mathematically stated relations between statistical and geometrical concepts including those between goodness-of-fit criteria for decision trees and data standardization, similarity and cons
出版日期Textbook 2019Latest edition
關(guān)鍵詞Clustering; Data Analysis; K-means; Principal component analysis; Visualization; data structures
版次2
doihttps://doi.org/10.1007/978-3-030-00271-8
isbn_softcover978-3-030-00270-1
isbn_ebook978-3-030-00271-8Series ISSN 1863-7310 Series E-ISSN 2197-1781
issn_series 1863-7310
copyrightSpringer Nature Switzerland AG 2019
The information of publication is updating

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Sebastian Henn,S?ren Koch,Gerhard W?scherpresenting target categories. Some related concepts such as Bayesian decision rules, bag-of-word model in text analysis, VC-dimension and kernel for non-linear classification are introduced too. The Chapter outlines several important characteristics of summarization and correlation between two featu
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Sebastian Henn,S?ren Koch,Gerhard W?scherand clusters. Spectral clustering gained popularity with the so-called Normalized Cut approach to divisive clustering. A relaxation of this combinatorial problem appears to be equivalent to optimizing the Rayleigh quotient for a Laplacian transformation of the similarity matrix under consideration.
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Core Partitioning: K-means and Similarity Clustering,d to yield what we call the complementary criterion. This criterion allows to reinterpret the method as that for finding big anomalous clusters. In this formulation, K-means is shown to extend the Principal component analysis criterion to the case at which the scoring factors are supposed to be bina
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Divisive and Separate Cluster Structures,and clusters. Spectral clustering gained popularity with the so-called Normalized Cut approach to divisive clustering. A relaxation of this combinatorial problem appears to be equivalent to optimizing the Rayleigh quotient for a Laplacian transformation of the similarity matrix under consideration.
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