Best Open Source Data Mining Software
This section covers data mining software. There’s a wide range of open source software available in this category.
In plain talk, data mining is a means to discover interesting knowledge from large quantities of data. It is an interdisciplinary field with contributions from many areas, such as statistics, machine learning, information retrieval, pattern recognition and bioinformatics. Data mining is widely used in many businesses including retail, telecommunications and finance.
|R is a programming language and software environment for statistical computing and graphics. And it excels at data mining; it's comprehensive set of data mining operations is mindbogglingly awesome.
License: GNU General Public License v2
|RapidMiner is a powerful and intuitive graphical user interface for the design of analysis processes. It offers a robust and powerful integrated environment for data preparation, machine learning, deep learning, text mining, and predictive analytics.
License: Affero General Public License
|Orange is billed as machine learning and data visualization for both the novice and expert alike. Interactive data analysis workflows with a large toolbox. It includes a range of data visualization, exploration, preprocessing and modeling techniques.
License: GNU General Public License v3
About Data Mining
Data mining (also known as knowledge discovery) is the process of gathering large amounts of information, analysing that information and condensing it into meaningful data. It brings together the fields of computer science, statistics and artificial intelligence.
Data mining is extremely important to the business community as it enables informed, knowledge-driven decisions to be taken. This is achieved by allowing institutions to visualise and understand their data, and to identify patterns and relationships that dictate business outcomes. Above all, data mining is recognised as an important tool for any business as it enables data to be converted into business intelligence. This intelligence can be used to generate accurate trends about customers’ purchasing behaviour, or to help in the assessment of customers’ credit rating. By embracing data mining technology, organisations can increase their revenue stream, help to minimise costs, as well as improving their competitive position. Data mining is also important in the fields of science and engineering, for surveillance, and in gaming (e.g. chess endgames).
There are dozens of different techniques that are used in data mining to examine and transform data. Some of the common techniques include decision trees, artificial neural networks, nearest neighbour method, generic algorithms, and rule induction.
In general terms, data mining consists of various techniques and algorithms for determining interesting patterns from large datasets. There are currently hundreds of different algorithms that perform tasks such as frequent pattern mining, clustering, and classification.
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