Every organization today depends on the features of data mining tools to plan their business decisions and cut costs significantly. Different data mining tools require different approaches. Some tools require no programming experience whilst some might require minimal coding experience.  There are ample data mining tools available in the market that are free and interesting to work with. Most of these tools do not require any explicit programming and can do the job with a mere drag and drop interface. 

Here’s a quick round-up of the popular data mining tools available in the market that are user-friendly and performance-oriented –

RapidMiner (Formerly Known as YALE )

With an exponentially growing base of over 200,000 users, support for more than 1500 operators and over 400 analytic functions for all data analysis and transformation tasks, and access to more than 40 different file types,  RapidMiner is definitely a go-to tool for data mining. RapidMiner is a ready-made, open-source, no-programming required data mining tool with advanced predictive analytic capabilities including text analytics, business analytics, machine learning, data mining, and data visualization. RapidMiner supports all the steps of the data mining process including validation, optimizations, and visualization of outcomes. It is an all-in-one powerful tool featuring hundreds of data preparation and machine learning algorithms needed to support almost all data mining projects. 

The basic idea behind the creation of RapidMiner is that the data analyst or the engineer need not be an expert at programming.  So, even if you have no experience in data mining or statistics, you can intuitively find a perfect graphical solution for your data. RapidMiner makes the data mining process transparent and smooth with a predefined set of operators that solve diverse problems. It also lets you collate and process information from diverse sources such as local files, databases, etc. Apart from the analytic functions and operators, there is also a RapidMiner server that can be used as a repository (cloud) for storing and executing various miner tasks. You can manage connections to the data sources by providing details of the miner tasks through the web interface of the server. The free edition of RapidMiner provides 10,000 rows of data and a logical processor.

R

R is one of the most cutting-edge data mining tools and a hands-down winner for all statistical computing and analysis tasks. If a statistical method exists, R definitely has a package implementing it.  The support for hundreds of libraries built specifically for data mining has made R a fan-favorite tool of data miners. You can perform data manipulation, data analysis, and data visualization, all using a single platform. The pre-built packages help you run and execute even the most advanced algorithms with ease.

Millions of researchers, data scientists, analysts, and big brands such as Microsoft, Mozilla, Ford Motor Group, Accenture, Facebook, Wipro, and Google are using R to solve complex issues across diverse sectors like  finance, e-commerce, healthcare, and more. R comes with huge community support of 2 million users. The empire of R language only seems to be growing exponentially, all thanks to its ease of use and extensibility. 

WEKA

Named after a flightless bird found on the islands of New Zealand, WEKA has a huge collection of machine learning algorithms for almost all data mining tasks. Waikato Environment for Knowledge Analysis (Weka) was developed by the Department of Computer Science, University of Waikato, New Zealand. If you are someone who has not done programming for quite some time, then WEKA, with its interactable GUI will provide you the easiest transition into data science. People who are experienced with Java can call the library directly into their own Java code while others can directly apply the algorithms to the dataset. It has tools for data preparation, regression, classification, clustering, association rules mining, feature selection, and visualization. Weka also provides access to SQL databases and further helps process the results returned by an SQL query.

SAS 

The need for data analysts and business analysts to be able to develop models in large numbers without having to depend on limited analytic modeling resources led to the development of SAS for analytics and data management. SAS can modify data, mine data, and manage data from various sources for statistical analysis through a GUI specifically designed for non-technical users. The distributed memory processing architecture makes SAS highly scalable and a perfect choice for text mining, data mining,  and optimization.

Even if you do not have an in-depth understanding of the techniques experience choosing attributes for predictive power, or time to fine-tune the model,  SAS provides a Rapid Predictive Modeler that does it all for you in a rapid automated way. The RPM calls SAS Enterprise Miner functions and runs automatically to fit diverse algorithms and select the best model based on the use case. The primary objectives of SAS RPM are  –

  • Help business analysts perform all mining tasks quickly.
  • Integrate data analytics with business intelligence for quick decision-making
  • Provide a single, integrated, and collaborative solution to solve the most complex analytical problems.

KNIME

KNIME is a free and open-source data mining platform built for complex analytics on a GUI based workflow. KNIME makes predictive analytics easily accessible even to naive users. KNIME lets you perform functions ranging from basic I/ O operations to data manipulations, data mining, and data transformations. KNIME bundles all functions of the data mining process in a single workflow. Features like scaling efficiency and quick deployment make it one of the best integration platform for analytics and reporting. KNIME is the topmost choice for people in pharmaceutical research and finance.

Orange

Orange is an open-source data analysis and visualization tool for data mining through Python scripting.  It is a perfect one-stop solution with components for all in-built machine learning algorithms, pre-processing of a data set, test and score feature to evaluate the accuracy of an algorithm on various datasets, and data visualization using graphs. It has components (known as widgets) for almost all popular machine learning algorithms, add-ons for text mining and bioinformatics, subset selection, pre-processing, and predictive modeling. Even novice users can use it without having to learn any programming language like C, C++, Java, or Python. The basic skills you must have to use Orange is a good grasp of various data mining concepts and knowledge on which algorithm should be used in a specific scenario. We are sure you will definitely fall in love with this tool’s fantastic visual programming features.

IBM SPSS Modeler

IBM SPSS Modeler’s intuitive GUI helps users visualize the data mining process with ease with little or no experience in programming. The drag and drop interface lets you build predictive models and algorithms to glean meaningful insights hidden in the data. The GUI provides access to both structured  (dates and number) and unstructured (text) data from multiple sources like survey data, files, and operational databases. This makes it easy to integrate and consolidate various types of datasets from diverse sources across the organization. A major USP of SPSS modelers is to climate unnecessary complexity involved in data transformations and simplify the usage of complex predictive models. It has support for more than 30 base machine learning algorithms along with enhanced support for various multithreaded analytical algorithms such as Two-step AS clustering,  Generalized Linear Engine, Random Trees, Tree-AS, and Linear AS. This tool comes with a 30-day free trial and if you really enjoy decision making using SPSS modeler, you can purchase the subscription to support your data mining projects.

H2O

Named among the Top 3 Vendors  in Artificial Intelligence and Machine Learning by industry analyst firm Enterprise Management Associates, H2O.ai has garnered popularity for its vision of creating a tool which lets almost everyone within the business to develop their own predictive models. With an open-source community of over  12,000 organizations, 129,000 data scientists, and more than half of the Fortune 500 companies using it – H20 has witnessed 330% growth in the last two years.H2O integrates perfectly with both the popular data science programming languages Python and R. Moreover, it lets users switch easily between Python, R and other data science tools while continuing to work on the same project. Its web-based interface Flow lets users import, export, and modify large datasets,  assess models performance, tweak various models, and much more. With its ability to build models faster, users get more time to experiment and play with data.

Apache Spark

Apache is an all-powerful, in-memory, distributed, and iterative open-source analytics engine that promises a clean, easy, and pleasurable experience for building parallel apps. It’s ease-of-use, speed, scalability, and high-performance analysis on large datasets have made it the fan-favorite of over 3000 companies including top players like Amazon, Visa, Oracle, Hortonworks, Verizon, and Cisco. Spark offers a visually appealing API with support for multiple programming languages like Python, Java, and R.   If you plan to pursue a career in Big Data or IoT, Spark should be a must-learn skill on your to-do list. 

Rattle

Clocking between 10,000 to 20,000 downloads a month, Rattle (R Analytical Tool To Learn Easily)  is a free and open-source GUI for beginners who want to perform data mining tasks with a mere point-and-click. All interactions through Rattle are stored as an R script which can also be executed directly without the Rattle GUI. You can also use it as a tool for learning and mastering your R programming skills by building your initial models in the Rattle interface.

It is the responsibility of the data analyst to choose an efficient and effective tool that can be of help all through the data mining process. So, before you choose a data mining tool for your task, make sure you choose a tool that works best for your project’s individual approach and also comprehends the data implications. 

If you think we’ve missed out any popular data mining tool that should have been on the list, let us know at sakshi@springboard.com.