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Laddar... Principles of Data Mining86 | Ingen/inga | 312,816 |
(3.67) | Ingen/inga | The first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics. The growing interest in data mining is motivated by a common problem across disciplines: how does one store, access, model, and ultimately describe and understand very large data sets? Historically, different aspects of data mining have been addressed independently by different disciplines. This is the first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics. The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for classification and regression, association rules, belief networks, classical statistical models, nonlinear models such as neural networks, and local "memory-based" models. The third section shows how all of the preceding analysis fits together when applied to real-world data mining problems. Topics include the role of metadata, how to handle missing data, and data preprocessing.… (mer) |
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Information från den engelska sidan med allmänna fakta. Redigera om du vill anpassa till ditt språk. To Crista, Aidan, and Cian
To Paula and Elsa
To Shelley, Rachel, and Emily | |
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Information från den engelska sidan med allmänna fakta. Redigera om du vill anpassa till ditt språk. The science of extracting useful information from large data sets or databases is known as data mining. | |
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Information från den engelska sidan med allmänna fakta. Redigera om du vill anpassa till ditt språk. Keogh and Smyth (1997) and Ge and Smith (2000) illustrate the use of probabilistic deformable templates for flexible modeling and detection of time series shapes with applications to interactive analysis of Shuttle sensor data and online monitoring of semiconductor manufacturing data. (Klicka för att visa. Varning: Kan innehålla spoilers.) | |
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▾Hänvisningar Hänvisningar till detta verk hos externa resurser. Wikipedia på engelska (2)▾Bokbeskrivningar The first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics. The growing interest in data mining is motivated by a common problem across disciplines: how does one store, access, model, and ultimately describe and understand very large data sets? Historically, different aspects of data mining have been addressed independently by different disciplines. This is the first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics. The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for classification and regression, association rules, belief networks, classical statistical models, nonlinear models such as neural networks, and local "memory-based" models. The third section shows how all of the preceding analysis fits together when applied to real-world data mining problems. Topics include the role of metadata, how to handle missing data, and data preprocessing. ▾Beskrivningar från bibliotek Inga biblioteksbeskrivningar kunde hittas. ▾Beskrivningar från medlemmar på LibraryThing
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