Feature Selection for Knowledge Discovery and Data Mining (The Springer International Series in Engineering and Computer Science, 454)
by Huan Huan Liu, Hiroshi Motoda, Huan Liu
Feature Selection for Knowledge Discovery and Data Mining, from The Springer International Series in Engineering and Computer Science, delves into techniques for selecting pertinent features in data mining. Authored by Huan Liu and Hiroshi Motoda, it supports efficient knowledge extraction from complex datasets, benefiting computer science and engineering professionals.
About This Book
Feature Selection for Knowledge Discovery and Data Mining is part of The Springer International Series in Engineering and Computer Science. Authored by Huan Liu and Hiroshi Motoda, it addresses key aspects of feature selection in data analysis.
The book focuses on methods to identify relevant features from large datasets, aiding in effective knowledge discovery processes. It serves as a valuable reference for researchers and practitioners in the field.
Published within a renowned series, this work contributes to advancements in engineering and computer science applications of data mining.
Reviews
No reviews yet. Be the first to review this book!