Data mining is also known as data exploration. It is a step in Knowledge-Discovery in Databases, a process of mining and analyzing large amounts of data and extracting information from it. Some of these applications include market segmentation-such as identifying the characteristics of a customer buying a specific product from a specific brand, fraud detection-identifying transaction patterns that may lead to online fraud, etc. In this article, we have compiled the 8 best open source tools for data mining.
As an open data mining platform, WEKA has assembled a large number of machine learning algorithms that can undertake data mining tasks, including data preprocessing, classification, regression, clustering, association rules, and visualization on a new interactive interface.
RapidMiner is the world’s leading data mining solution, with advanced technology to a very large extent. Its data mining tasks cover a wide range, including various data arts, which can simplify the design and evaluation of the data mining process.
Orange is a component-based data mining and machine learning software package. Its functions are friendly, powerful, fast and multi-functional visual programming front end for browsing data analysis and visualization, and it is based on Python for script development. . It contains a complete series of components for data preprocessing, and provides data accounting, transition, modeling, model evaluation and exploration functions. It is developed by C++ and Python, and its graphics library is developed by the cross-platform Qt framework.
KNIME (Konstanz Information Miner) is a user-friendly, intelligent, and open source platform for data integration, data processing, data analysis and data exploration.
jHepWork is a complete set of object-oriented scientific data analysis framework. Jython macros are used to display one-dimensional and two-dimensional histogram data. The program includes many tools that can be used to interact with two-dimensional and three-dimensional scientific graphics.
6. Apache Mahout
Apache Mahout is a brand new open source project developed by the Apache Software Foundation (ASF). Its main goal is to create some scalable machine learning algorithms for developers to use for free under the Apache license. The project has reached its second year and currently only has one public release. Mahout contains many implementations, including clustering, classification, 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 a data mining platform similar to weka, written in java, with a GUI graphical interface. Can be used to find outliers.
Rattle (an easy-to-learn R analysis tool) provides statistical and visual summaries of data, converts data into easy-to-model forms, constructs unsupervised and supervised models from the data, presents the performance of the model graphically, and obtains new data set.