Data mining is a field that implies analyzing large data sets in order to discover new patterns and methods for database management, data processing and inference considerations.
Weka is a package that offers users a collection of learning schemes and tools that they can use for data mining. The algorithms that Weka provides can be applied directly to a dataset or your Java code.
When running the program, you can view four available applications that you can access: 'Explorer', 'Experimenter', 'KnowledgeFlow' and 'Simple CLI'.
The first section allows you to open a dataset or a database and edit it as you wish. You can filter the data contents, change the attributes and visualize the result in a bar chart. Also, you can classify the available data according to a predefined set of rules, as well as perform a complete cost / benefit analysis that automatically displays the cost matrix and the threshold curve.
In addition to this, the program also includes tools for data clustering, association rules and attributes evaluator. Furthermore, you can use it for data plotting, as it allows you to view and analyze point graphs for each possible attribute combination.
The program is also suitable for developing new machine learning schemes. You just have to configure your experiment by choosing its type: classification or regression. Also, you have to choose the desired dataset and algorithm and then you can run it. The results can be saved either in ARFF or CSVformats or as a JDBC database.
Also, you can analyze and test a data file. The program allows you to choose the significance and the comparison field, as well as the sorting criteria and the test base.
Weka is an easy to use application, yet it is designed for those who are familiar with data mining procedures and database analysis. Using this software, you can view and analyze ARFF data files, as well as perform data clustering and regression.

 

 

 

 

 

 

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Weka is a free and open-source software developed by University of Waikato in New Zealand. It is the main package used by the Weka project.
The Weka is a flexible and powerful data mining and machine learning software. It is open-source and platform independent software.
Weka provides support for common learning schemes such as multi-class classification, multi-label classification, ordinal regression and regression, ensemble learners, rule induction, clustering techniques and association rule mining.
More specifically, the Weka enables you to perform various tasks such as data preprocessing, data transformation, data discretization, feature selection, model building and evaluation. Also, you can use it to train supervised learning schemes including classification, regression, multi-class classification, multi-label classification, ordinal regression, support vector machines, naive Bayes, and artificial neural networks.
Moreover, you can use Weka in order to perform unsupervised learning using clustering techniques like k-means, hierarchical clustering, and a mixture of Gaussians.
Therefore, you can easily build your own models by creating machine learning schemes that can be applied using Weka.
Weka is a user friendly data mining software. Indeed, the software offers a set of applications that allows you to perform different data analysis tasks.
Moreover, the software is built on a user-friendly interface that allows you to easily access all available algorithms.
When you launch the software, you can load and view all available data for analysis. Then you can perform a data preprocessing or an exploration using data visualization tools.
Besides, you can use Weka to define your own learning schemes as well as to run them. In addition, the software is equipped with different prediction tools that allow you to predict your data without the need of a model.
You can also perform a data filtering and an out-of-sample data validation using Weka. Furthermore, you can examine the different data transformations and preprocessing techniques.
Moreover, the software is designed to allow you to create and run the different learning schemes, as well as to create and run the different prediction models.

Weka is a free and open-source software developed by University of Waikato in New Zealand. It is the main package used by the Weka project.
The Weka is a flexible and powerful data mining and machine learning software. It is open-source and platform independent software.
Weka provides support for common learning schemes
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Weka is a toolkit for building data mining systems. Its main objective is to offer a complete set of tools for data mining tasks. Currently, weka provides a set of learning methods and tools for:
1 – data mining with a supervised or an unsupervised learning algorithm
2 – pattern discovery algorithms, including direct search and association methods
3 – classification algorithms
4 – regression methods
5 – clustering methods
6 – classifier-ranking based methods
7 – prediction of new problems with a classification algorithm
8 – model building with any inductive learning algorithm
9 – cross-validation and performance evaluation methods
10 – data visualization methods for multidimensional data
The program implements a set of XML-based description formats for the data to be used in WEKA for learning process.
The WEKA data mining kit includes the following WEKA application programs:
Data Mining Explorer
You can use WEKA Explorer to explore Weka data mining kit. Its main objective is to view WEKA data mining kit containing database and allow you to browse its contents.
Weka Explorer is divided into the following sections:
* Weka Explorer Interface
* Explorer Data View
* Explorer Database View
* Data View
* Database View
The Explorer Data View allows you to browse the available data mining kit. You can access this section by expanding the selected bundle or by clicking on it from the Explorer interface.
The Explorer Database View allows you to visualize the data in WEKA data mining kit according to the described data mining scheme.
The Explorer Database View consists of the following sections:
* Database Home
* Database Root
* Database Explorer
* Details of Input Data
* Details of model Performance
* Details of model Decision Performance
* Details of model Prediction Performance
* Details of model Explanation
* Details of model Statistics
* Data Explorer
* Dataset Explorer
* Explorer Model Configuration
* Explorer View
* Explorer Check
* Explorer Editor
* Explorer Filter
The Explorer Explorer is used to apply different data mining schemes. You can access this section by expanding a selected bundle.
The Explorer Explorer is divided into the following sections:
* Explorer Dataset
* Explorer Split File
* Explorer Classifier
* Explorer Simple Regression
* Explorer Simple Regression – Correlation
* Explorer Simple Regression – Association
* Explorer Simple Classification
* Explorer Simple Classification – ROC
* Explorer Model Builder
* Explorer Model Builder – E-Learning Package
* Explorer

What’s New in the Weka?

===============
Weka is a package that offers users a collection of learning schemes and tools that they can use for data mining. The algorithms that Weka provides can be applied directly to a dataset or your Java code.
When running the program, you can view four available applications that you can access: ‘Explorer’, ‘Experimenter’, ‘KnowledgeFlow’ and ‘Simple CLI’.
The first section allows you to open a dataset or a database and edit it as you wish. You can filter the data contents, change the attributes and visualize the result in a bar chart. Also, you can classify the available data according to a predefined set of rules, as well as perform a complete cost / benefit analysis that automatically displays the cost matrix and the threshold curve.
In addition to this, the program also includes tools for data clustering, association rules and attributes evaluator. Furthermore, you can use it for data plotting, as it allows you to view and analyze point graphs for each possible attribute combination.
The program is also suitable for developing new machine learning schemes. You just have to configure your experiment by choosing its type: classification or regression. Also, you have to choose the desired dataset and algorithm and then you can run it. The results can be saved either in ARFF or CSVformats or as a JDBC database.
Also, you can analyze and test a data file. The program allows you to choose the significance and the comparison field, as well as the sorting criteria and the test base.
Weka is an easy to use application, yet it is designed for those who are familiar with data mining procedures and database analysis. Using this software, you can view and analyze ARFF data files, as well as perform data clustering and regression.
Weka Description:
===============

2

v0.9.4

October 2, 2016

Weka is an open source machine learning library. Its goal is to provide an easy to use, effective and reliable framework for data mining applications. It is an acronym for Waikato Environment for Knowledge Analysis. It is a product of University of Waikato in New Zealand. In 1999 a group of researchers from UNED (Upper Normandy Education Department) started the implementation of the algorithm on their own time.
Weka development is partially funded by University of Waikato and other institutions. The latest contributions and contributions are related to the implementation of classification, association, regression, clustering methods.
Weka

System Requirements:

Minimum:
OS: Windows 7, 8 or 10
Processor: Intel i3-2xx or AMD dual-core processor
Memory: 1 GB RAM
Recommended:
Processor: Intel i5-3xx or AMD dual-core processor
Memory: 2 GB RAM
Recommended DirectX:
DirectX 10
Video card: NVIDIA GeForce GTX 460 or AMD HD 7670
NVIDIA Geforce GTX 480 or AMD HD 7970
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