Name: Data Mining: Theories, Algorithms, and Examples (en Inglés)
Price: 171580.00 CLP
Availability: InStock
Author: Ye, Nong
ISBN: 9781439808382

portada Data Mining: Theories, Algorithms, and Examples (en Inglés)

Formato

Libro Físico

Autor

Nong Ye

Editorial

CRC Press

Idioma

Inglés

N° páginas

349

Encuadernación

Tapa Dura

Dimensiones

23.4 x 16.0 x 2.5 cm

Peso

0.64 kg.

ISBN13

9781439808382

N° edición

Categorías

Econometría
Técnicas de gestión empresarial
Ingeniería: general
Data mining: minería de datos

Data Mining: Theories, Algorithms, and Examples (en Inglés)

Nong Ye (Autor) · CRC Press · Tapa Dura

Libro Físico

$ 171.580

$ 311.970

Ahorras: $ 140.390

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Origen: Reino Unido (Costos de importación incluídos en el precio)

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Reseña del libro "Data Mining: Theories, Algorithms, and Examples (en Inglés)"

New technologies have enabled us to collect massive amounts of data in many fields. However, our pace of discovering useful information and knowledge from these data falls far behind our pace of collecting the data. Data Mining: Theories, Algorithms, and Examples introduces and explains a comprehensive set of data mining algorithms from various data mining fields. The book reviews theoretical rationales and procedural details of data mining algorithms, including those commonly found in the literature and those presenting considerable difficulty, using small data examples to explain and walk through the algorithms.The book covers a wide range of data mining algorithms, including those commonly found in data mining literature and those not fully covered in most of existing literature due to their considerable difficulty. The book presents a list of software packages that support the data mining algorithms, applications of the data mining algorithms with references, and exercises, along with the solutions manual and PowerPoint slides of lectures. The author takes a practical approach to data mining algorithms so that the data patterns produced can be fully interpreted. This approach enables students to understand theoretical and operational aspects of data mining algorithms and to manually execute the algorithms for a thorough understanding of the data patterns produced by them.