Best open source data mining tools

It is written using java language. The fast miner can be used for predictive analysis, business application, education and research, commercial applications, etc. It increases the speed of delivery as it follows the template framework. It not only increases the delivery speed but also reduces errors while transforming. It is open-source software written in python language. Orange is the best software for analyzing data and machine learning.

These components are called widgets. These widgets are used for reading data, analyzing components, allowing users to select the features, and showing the data. With orange, data formatting and moving them with the help of widgets becomes fast and easy. The University of Waikato develops weka.

It is an open-source software used for predictive modelling and analysis of data. Weka has a GUI interface that provides easy and interactive access to users.

It supports SQL and allows a user to connects to the database, and performs operations by firing query. It stores data in a flat-file format. It is built by combining data mining and machine learning components. It has been used for pharmaceutical research, business intelligence, and financial analysis. It is not open-source software; it is licensed software, and we have to purchase the license to use this.

Small and large organizations use Sisense to handle the data. As it also supports widgets like orange, it is easy to move data and creates reports by dragging and dropping. Not even technical people can work with Sisense as its GUI based. With the help of widgets, Sisense generated words are in the form of bar chart, pie chart, line chart, etc. Know more here. About: Waikato Environment for Knowledge Analysis, or as we know it — WEKA is another open-source machine learning tool that can be used for association rule mining.

It can be accessed via a graphical user interface or standard terminal applications. Alongside, it can also be accessed through a Java API and can be used for preprocessing data implementation of ML algorithm, and data visualisation for any platform. WEKA consists of various ML algorithms that can be leveraged for solving real-world data mining problems. About: Another open-source data mining tool for academic and research purposes is the Tanagra tool.

It not only helps in association rule mining but also includes factorial analysis, clustering, parametric and non-parametric statistics etc.

From exploratory data analysis to machine learning, this tool is capable of performing several data mining methods. One can access the source code and add his own algorithm to use Tanagra. About: Developed by the company of the same name — RapidMiner is another open-source tool renowned for its easy to use visual environment for predictive analytics. It allows the user to connect to any data source whether it be enterprise data warehouses, data ages, cloud storages, business applications and social media.

Along with that, RapidMiner comes with automated in-database processing where it runs data prep and ETL inside databases to optimise the data for analysis. This intuitive analytical platform supports many languages and comprises an integrated development environment and an extensible plug-in system. Not only can it be used for building machine learning models and optimise its performance, but also validate models, explain ML models and make predictions.

About: Lastly, FrIDA, an open-source tool for developers, reverse engineers and security researchers that allow them to process data. Users can use the tools directly from C along with multiple language bindings — Node. NET, Qml etc. Apriori is a Join-Based algorithm and FP-Growth is Tree-Based algorithm for frequent itemset mining or frequent pattern mining for market basket analysis. Initially developed by Intel, OpenCV is an open-source computer vision cross-platform library for real-time image processing and which has become a standard tool for all things related to computer vision applications.

In this article, we explore the best open source tools that can aid us in data mining. Data mining, also known as knowledge discovery from databases, is a process of mining and analysing enormous amounts of data and extracting information from it.

Data mining can quickly answer business questions that would have otherwise consumed a lot of time. Some of its applications include market segmentation — like identifying characteristics of a customer buying a certain product from a certain brand, fraud detection — identifying transaction patterns that could probably result in an online fraud, and market based and trend analysis — what products or services are always purchased together, etc.

This article focuses on the various open source options available and their significance in different contexts. Pre-processing: This involves all the preliminary tasks that can help in getting started with any of the actual mining tasks. Classification: This is tagging or classifying data items into different user-defined categories.

Outlier analysis helps in identifying those data elements which are deviant or distant from the rest of the elements in a dataset. This can help in anomaly detection. Associative analysis helps in bringing out hidden relationships among data items in a large data set. This can help in predicting the occurrence of a particular item in a transaction or an event whenever some other item is present.

You can think of this as a conditional probability. Regression is used to predict values of a dependent variable by constructing a model or a mathematical function out of independent variables. Summarisation helps in coming up with a compact description for the whole data set. Data mining is a combination of various techniques like pattern recognition, statistics, machine learning, etc.

While there is a good amount of intersection between machine learning and data mining, as both go hand in hand and machine learning algorithms are used for mining data, we will restrict ourselves in this article to only those data mining tools. It comprises a collection of machine learning algorithms for data mining. It packages tools for data pre-processing, classification, regression, clustering, association rules and visualisation. Explorer is a user-friendly graphical interface for two-dimensional visualisation of mined data.

It lets you import the raw data from various file formats, and supports well known algorithms for different mining actions like filtering, clustering, classification and attribute selection. However, when dealing with large data sets, it is best to use a CL based approach as Explorer tries to load the whole data set into the main memory, causing performance issues.

This software also provides a Java Appetiser for use in applications and can connect to databases using CJD. Weka has proved to be an ideal choice for educational and research purposes, as well as for rapid prototyping.

Rapid Miner is available in both FOSS and commercial editions and is a leading predictive analytic platform. Gartner, the US research and advisory firm, has recognised Rapid Miner and Knife as leaders in the magic quadrant for advanced analytic platforms in Rapid Miner is helping enterprises embed predictive analysis in their business processes with its user friendly, rich library of data science and machine learning algorithms through its all-in-one programming environments like Rapid Miner Studio.

Besides the standard data mining features like data cleansing, filtering, clustering, etc, the software also features built-in templates, repeatable work flows, a professional visualisation environment, and seamless integration with languages like Python and R into work flows that aid in rapid prototyping.

The tool is also compatible with weak scripts. Python users playing around with data sciences might be familiar with Orange. It is a Python library that powers Python scripts with its rich compilation of mining and machine learning algorithms for data pre-processing, classification, modelling, regression, clustering and other miscellaneous functions.