Name: Data Mining (en Inglés)
Price: 84570.00 CLP
Availability: InStock
Author: Vignesh, N. Aswin ; Amalarethinam, D. I. George
ISBN: 9786138930594

Formato

Libro Físico

Autor

D. I. George Amalarethinam

N. Aswin Vignesh

Editorial

Scholars' Press

Idioma

Inglés

N° páginas

156

Encuadernación

Tapa Blanda

Dimensiones

22.9 x 15.2 x 0.9 cm

Peso

0.24 kg.

ISBN13

9786138930594

Categorías

Tecnología de la información: cuestiones generales

Data Mining (en Inglés)

D. I. George Amalarethinam (Autor) · N. Aswin Vignesh (Autor) · Scholars' Press · Tapa Blanda

Libro Nuevo

$ 84.570

$ 153.760

Ahorras: $ 69.190

45% descuento

Envío normal

Origen: Estados Unidos (Costos de importación incluídos en el precio)

Se enviará desde nuestra bodega entre el Jueves 25 de Julio y el Martes 06 de Agosto.

Lo recibirás en cualquier lugar de Chile entre 1 y 3 días hábiles luego del envío.

Reseña del libro "Data Mining (en Inglés)"

Data classification problem is studied by statisticians and researchers of machine learning researchers. Data classification is widely used in variety of Engineering and scientific disciplines such as biology, psychology, medicines, marketing, computer vision and artificial intelligence. The goal of the data classification is to classify objects into a number of categories or classes. The task of classification is to assign a class to the data object in a given dataset. According to Pima Indians data set, the highest reported disease in the world is diabetes. The classification technique is used to classify the patterns from the large databases into predefined set of classes. This research has proposed four different classification techniques, namely, Regular Covering Technique (RCT), Stipulation Technique with Advanced Decision tree (STAD) model, Iterative Technique with J48 scion (ITJ) method and Monotony Advanced Decision tree Graft (MADG) algorithms. Regular Covering Technique (RCT) generates classification rules from both continuous and discrete datasets. In this book, various algorithms have been proposed to enhance the classification techniques.