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Titlebook: Clusters, Orders, and Trees: Methods and Applications; In Honor of Boris Mi Fuad Aleskerov,Boris Goldengorin,Panos M. Pardalos Book 2014 Sp

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書(shū)目名稱(chēng)Clusters, Orders, and Trees: Methods and Applications
副標(biāo)題In Honor of Boris Mi
編輯Fuad Aleskerov,Boris Goldengorin,Panos M. Pardalos
視頻videohttp://file.papertrans.cn/229/228571/228571.mp4
概述Contains new models and algorithms for knowledge discoveries.Features new tools for developing practical algorithms for solving problems in data analysis.Opens a new direction in addressing difficult
叢書(shū)名稱(chēng)Springer Optimization and Its Applications
圖書(shū)封面Titlebook: Clusters, Orders, and Trees: Methods and Applications; In Honor of Boris Mi Fuad Aleskerov,Boris Goldengorin,Panos M. Pardalos Book 2014 Sp
描述.The volume is dedicated to Boris Mirkin on the occasion of his 70th birthday. In addition to his startling PhD results in abstract automata theory, Mirkin’s ground breaking contributions in various fields of decision making and data analysis have marked the fourth quarter of the 20th century and beyond.?Mirkin has done pioneering work in group choice, clustering, data mining and knowledge discovery aimed at finding and describing non-trivial or hidden structures—first of all, clusters, orderings and hierarchies—in multivariate and/or network data..This volume contains a collection of papers reflecting recent developments rooted in Mirkin’s fundamental contribution to the state-of-the-art in group choice, ordering, clustering, data mining and knowledge discovery. Researchers, students and software engineers will benefit from new knowledge discovery techniques and application directions..
出版日期Book 2014
關(guān)鍵詞artificial intelligence; data analysis; mathematical programming; operations research; theory of computi
版次1
doihttps://doi.org/10.1007/978-1-4939-0742-7
isbn_softcover978-1-4939-4799-7
isbn_ebook978-1-4939-0742-7Series ISSN 1931-6828 Series E-ISSN 1931-6836
issn_series 1931-6828
copyrightSpringer Science+Business Media New York 2014
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High-Dimensional Data Classificationd and their relative merits and drawbacks are examined. Lastly, we describe AdaBoost and Random Forests in the ensemble classifiers and discuss their recent surge as useful algorithms for solving high-dimensional data problems.
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Weak Hierarchies: A Central Clustering Structure and thus an optimal closed weak hierarchy by means of the bijection between quasi-ultrametrics and (indexed) closed weak hierarchies. Furthermore, we highlight the relationship between weak hierarchical clustering and formal concepts analysis, through which concept extents appear to be weak cluster
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https://doi.org/10.1007/978-3-7091-7860-7 Problem is able to find an exact optimal solution for markets with at most 1,000 stocks by means of general purpose solvers like CPLEX. We have designed and implemented a high-quality greedy-type heuristic for large-sized (many thousands of stocks) markets. We observed an important “median nesting”
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Clusters, Orders, and Trees: Methods and ApplicationsIn Honor of Boris Mi
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