The Top 10 Open Source Data Mining Tools

The age of big data requires exploiting the potential of information by enterprises, scientists, and people in general. They enable users to find out some priceless hints which are present in the data. This article will discuss a number of important open source data mining tools necessary for digging out the unknown and the known aspects of your datasets.

Table of content: 

• What are data Mining Tools?
• Weka
• KNIMI
• Orange
• RapidMiner
• Apache Mahout
• AScikit Learn
• AJHepWork
• ADataMelt 
• BIRT
• AELKI

What are Data Mining Tools?

Open source data mining tools are usually libraries of freeware programs or program sections meant for the extraction of insights, patterns, and knowledge from multiple datasets. These tools enjoy widespread use across different domains such as business, education, academic research among others that facilitate empirical informed decision making. These are commonly built and hosted by open-source communities with features of transparency, adaptability, affordability among others.

Weka

Weka is a conveniently freeware tool for data mining with its complete collection of machine learning procedures and data preprocessing options. It is very prevalent among the research as well as the academic circles.

Key features:

1. The graphical user interface in WEKA makes it friendly even for novices while not undermining expertise.

1. The kit provides all machine leaning methods such as classifications, regressions, segmentation and even association rules.
2. Feature selection, data visualization, and pre-processing utilities are available in WEKA.
3. WEKA is commonly employed by universities and research organizations and can read different dataset formats.

KNIME

KNIME is an open source tool that includes a data-analytic reporting and data integration platform. It enables construction of data flows making a user be able to combine different methods of data mining as well analysis. As mentioned earlier, KNIME has excellent features such as flexibility and user-friendliness.

Key features:

1. KNIME is an all-inclusive platform providing data analytics, reporting and data integration thus serving multiple needs of databases.
2. Workflow-Based Approach: By enabling users to design data workflows that smoothly fuse data mining and analysis methodologies, it helps to effortlessly blend these two techniques together.
3. KNIME has an intuitive GUI that can be easily operated by individuals with varied skills in technology.
4. This is a very comprehensive repository of extensions and integrations that include machine learning, text mining, and image processing.

Orange

Orange is open source Data visualization and analysis tool. Its consists of several data mining and machine learning components. The difference lies in the visual user interface, making it good for all those with different tech understanding.

Key features:

1. The data visualization, analysis, and exploration tool named orange is a data exploration tool.
2. It has a Graphical User Interface (GUI) making it simpler to establish data analysis procedures.
3. Orange provides multiple options in terms of data mining and machine learning, therefore, it is an easy-to-use tool for quick testing.
4. Its user-friendly interface is suited for users with varied technical expertise.

RapidMiner

Rapid Minder is a robust open source data science platform capable for providing data mining, machine learning and advanced analytical tools. This software has an easy-to-use interface that caters to everyone, expert or amateur.

Key features:

1. The data science tool called RapidMiner deals with all aspects, including data mining, machine learning, and advanced analytics.
2. It has a user-friendly interface that caters to beginner and expert users alike.
3. The platform has a wide range of machine learning algorithms in its library of algorithms.
4. It caters for the whole data science continuum ranging from data munging to modelling to deployments.

Apache Mahout

Apache Mahout deals with scalable machine learning and data mining. It was made for handling huge databases and suits well for developing a recommendation engine or cluster.

Key features:

1. Apache Mahout focuses on scalability of machine learning especially using large data sets.
2. However, it is widely known for developing recommender systems and cluster algorithms.
3. Due to this, Apache Mahout fits well in Hadoop and MapReduce platforms that process big data.
4. There are several recommendations task supporting collaborative filtering algorithms.

Scikit learn

Scikit learn is one of these topmost machine learning libraries in python Although an individual data mining tool, it boasts a powerful assortment of machine learning algorithms that are useful for analysis and modeling of data.

Key features:

1. A commonly used machine learning library in python named Scikit Learn has many different types of machine learning algorithms.
2. Data scientists and developers have their favourite for this reason because it deals with code readability, usability and model’s quality.
3. The open-source community supports it and writes for its documentation.

JHepWork

JHepWork is an open source data analysis framework, that relies on Java. Various data analysis and visualization tools are available through it and it can serve as an essential tool in research and scientific studies.

Key features:

1. It is an open-source, Java-based data analysis framework known as JHepWork.
2. Data analysis and visualization can be tailored for researchers and scientists in such an application.
3. JHepWork is user-friendly and can be adopted towards specific demands of research activities and individual tastes.

DataMelt

DataMelt is an open source frame work for scientific data analysis, mathematical modelling, and machine learning. It is a software package meant for data processing and graphics, has several numerical and statistics packages.

Key Fetures:

1. DataMelt is a platform for scientific data analysis, mathematical modelling and computational intelligence (machine learning).
2. The software provides many types of numerical and statistical techniques for the analysis and modelling of data.
3. DataMelt is used for analysis and representation of different kinds of information associated with various science disciplines.
4. Its functionality can be extended by use of plugins and custom code by users Creative Commons.

BIRT (Business Intelligence and Reporting Tools)

BIRT is a popular open source reporting, analytics and dashboard tool. It is a component of the Eclipse project, for business intelligence and data analysis.
Open source Business Intelligence and reporting tools, also known as BIRT.

Key Features:

1. It is a reporting tool that can allow an organization to develop interactive reports for use in dashboard.
2. It is flexible and interfaces with other data sources and databases; thus, suitable for the business intelligence and data analysis purposes.

ELKI(Environment for Development KDD-Applications Support by Index-Structures)

ELKI  is an open source data mining framework, specializes on unsupervised learning and clustering. It provides immense strength and capacity, thus perfect for big data mining operations.

Key Features:

1. ELKI is centered on unsupervised learning and clustering algorithms.
2. It is renowned for its strength and flexibility, which makes it perfect for big data mining projects.
3. The modular architecture of ELKI makes it extremely flexible such that its users can tailor it towards their individual requirements.

Conclusion:

Open source data mining tool is essential for transforming available data into valuable information that can take necessary actions based on it. These tools provide an opportunity for a data scientist, a business analyst, or a researcher to identify the occurrence patterns among different variables in data. All these tools have various pros and cons thus the suitability of each of them is determined by the nature of your data and the kind of output you are targeting. Trial some of these open source data mining tools for purposes of finding the most suitable tool to use in analyzing specific data sets.

 Also read:

Top 5 AI Tools You need to know as a Data Analyst