8 Best Open Source Data Mining Tools
Data mining is a concept first realized when businesses began storing important information on computer databases and extracting useful information from large sets of data. It is a fairly new method that can only be described as discovering hidden values from within a large amount of unknown data. Data mining is a reliable way for different companies to predict future trends and behaviors from within their business, allowing them to make better decisions going forward. In this article, i have compiled the eight best open source tools for data mining.
WEKA as a public data mining work platform, a collection of a large number of data mining tasks can bear the machine learning algorithm, including data preprocessing, classification, regression, clustering, association rules and visualization in the new interactive interface.
2, Rapid Miner
RapidMiner is the world’s leading data mining solution, with a very large degree of advanced technology. Its data mining tasks cover a wide range of data arts, which can simplify the design and evaluation of data mining processes.
Orange is a component-based data mining and machine learning software suite that features friendly, powerful, fast and versatile visual programming front-end for browsing data analysis and visualization, with Python binding for scripting The It contains a complete set of components for data preprocessing and provides data accounting, transition, modeling, pattern assessment and exploration capabilities. Developed by C ++ and Python, its graphics library is by a cross-platform Qt framework .
KNINE (Konstanz Information Miner) is a user-friendly, intelligent, and open source data integration, data processing, data analysis and data exploration platform.
JHepWork is a complete set of object-oriented scientific data analysis framework. The Jython macro is used to display data for one- and two-dimensional histograms. The program includes a number of tools that can be used to interact with two-dimensional, three-dimensional scientific graphics.
Apache Mahout is a brand new open source project developed by the Apache Software Foundation (ASF) with the primary goal of creating scalable machine learning algorithms for developers to use without permission from Apache. The project has grown to its two-year year, with only one public release. Mahout contains many implementations, including clustering, categorization, CP, and evolutionary programs. In addition, by using the Apache Hadoop library, Mahout can be effectively extended to the cloud.
ELKI (Environment for Developing KDD-Applications Supported by Index-Structures) is mainly used to cluster and find outliers. ELKI is similar to weka data mining platform, written in java, GUI graphical interface . Can be used to find outliers.
Rattle (easy-to-learn R analysis tool) provides statistical and visualization of data, transforms data into easy-to-form form, builds unsupervised and supervised models from data, graphically presents model performance, and draws new data set.