Author: Zen and the Art of Computer Programming
1 Introduction
Weka is a Java environment software package developed by Stanford University for data mining, statistical analysis and machine learning. Its unique GUI-based interaction model and rich plug-in system make it the first choice of many data scientists and scholars. tool. Its functions cover data preprocessing, feature selection, classification, regression, clustering, association rules, time series prediction, etc. At the same time, Weka also provides an easy-to-use Graphical User Interface (GUI) that can be used to quickly set parameters, browse results, and create reports.
Weka has been widely used in data mining, information retrieval, bioinformatics, computer vision, image processing, text mining, geographic information systems, genetic planning, network security, health care, financial analysis and other fields, and has been favored by many scholars and enterprises. recognition. Its researchers and practitioners at home and abroad are increasingly using Weka to conduct related work.
This article will introduce some features and main advantages of Weka, and lead readers to understand the basic concepts, terminology, core algorithm principles and specific operation methods of Weka, from the most basic preprocessing to data integration methods. Finally, let’s talk about Weka’s future development direction and possible problems.
2.Basic concepts, terminology, concepts
2.1 Weka
2.1.1 Definition
Weka is an open source machine learning platform that can run various algorithms and provides a good interactive interface. Weka is a free, open source project maintained and updated by the Stanford University Information Science Laboratory. The latest version is currently 3.9.2.
2.1.2 Why use Weka?
Weka is able to do this because it has several advantages:
Scalability Weka adopts a plug-in structure, allowing developers to extend its functionality by adding new algorithms or modules.
Portability Weka supports multiple platforms, including Windows, M