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Titlebook: Core Concepts in Data Analysis: Summarization, Correlation and Visualization; Boris Mirkin Textbook 20111st edition Springer-Verlag London

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發(fā)表于 2025-3-21 19:57:57 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱(chēng)Core Concepts in Data Analysis: Summarization, Correlation and Visualization
編輯Boris Mirkin
視頻videohttp://file.papertrans.cn/239/238229/238229.mp4
概述Provides an in-depth understanding of a few basic techniques in data analysis rather than covering the broad spectrum of approaches developed to date..Explores methodical innovations of summarization
叢書(shū)名稱(chēng)Undergraduate Topics in Computer Science
圖書(shū)封面Titlebook: Core Concepts in Data Analysis: Summarization, Correlation and Visualization; Boris Mirkin Textbook 20111st edition Springer-Verlag London
描述.Core Concepts in Data Analysis: Summarization, Correlation and Visualization. .provides in-depth descriptions of those data analysis approaches that either summarize data (principal component analysis and clustering, including hierarchical and network clustering) or correlate different aspects of data (decision trees, linear rules, neuron networks, and Bayes rule)..Boris Mirkin takes an unconventional approach and introduces the concept of multivariate data summarization as a counterpart to conventional machine learning prediction schemes, utilizing techniques from statistics, data analysis, data mining, machine learning, computational intelligence, and information retrieval..Innovations following from his in-depth analysis of the models underlying summarization techniques are introduced, and applied to challenging issues such as the number of clusters, mixed scale data standardization, interpretation of the solutions, as well as relations between seemingly unrelated concepts: goodness-of-fit functions for classification trees and data standardization, spectral clustering and additive clustering, correlation and visualization of contingency data. ?.?The mathematical detail is enca
出版日期Textbook 20111st edition
關(guān)鍵詞Clustering; Data Analysis; K-means; Principal component analysis; Visualization
版次1
doihttps://doi.org/10.1007/978-0-85729-287-2
isbn_ebook978-0-85729-287-2Series ISSN 1863-7310 Series E-ISSN 2197-1781
issn_series 1863-7310
copyrightSpringer-Verlag London Ltd., part of Springer Nature 2011
The information of publication is updating

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發(fā)表于 2025-3-21 20:53:29 | 只看該作者
1D Analysis: Summarization and Visualization of a Single Feature,ssible: just one feature. This also provides us with a stock of useful concepts for further material. The concepts of histogram, central point and spread are presented. Two perspectives on the summaries are outlined: one is the classical probabilistic and the other of approximation, naturally extend
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Hierarchical Clustering, different algorithms for divisive clustering, all three based on the same square error criterion as K-Means partitioning method. Agglomerative clustering starts from a trivial set of singletons and merges two clusters at a time. Divisive clustering splits clusters in parts and should be a more inte
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發(fā)表于 2025-3-22 23:33:57 | 只看該作者
Approximate and Spectral Clustering for Network and Affinity Data, chapter describes methods for finding a cluster or two-cluster split combining three types of approaches from both old and recent developments: (a)combinatorial approach that is oriented at clustering as optimization of some reasonable measure of cluster homogeneity, (b)additive clustering approach
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發(fā)表于 2025-3-23 01:45:26 | 只看該作者
Web Mining and Recommendation SystemsKohonen self-organizing maps (SOM) that tie up the sought clusters to a visually convenient two-dimensional grid. Equivalent reformulations of K-Means criterion are described – they can yield different algorithms for K-Means. One of these is explained at length: K-Means extends Principal component a
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發(fā)表于 2025-3-23 08:19:59 | 只看該作者
K-Means and Related Clustering Methods,Kohonen self-organizing maps (SOM) that tie up the sought clusters to a visually convenient two-dimensional grid. Equivalent reformulations of K-Means criterion are described – they can yield different algorithms for K-Means. One of these is explained at length: K-Means extends Principal component a
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