Companies today collect a massive amount of data and try to analyze customer behavior so as to make enhanced products. The data, when generated, is in the raw format and usually unstructured. Unless someone can analyze that data and transform it into a usable format, raw data is of no use. The demand for such skilled professionals has surged over the years, and they even command high paychecks. Those who take a data scientist course at Simplilearn become more employable for such data-related roles.
When you learn more about data, it is inevitable to come across the term data mining. It is the process of searching large amounts of data to identify patterns and correlations that go beyond plain analysis. Professionals who perform data mining use complex mathematical algorithms to segment the data and analyze the probability of future events. Data mining can give answers to business questions that were previously too time-consuming to resolve manually. Businesses are now leveraging data mining and machine learning to enhance many business processes like sales, investments, and understanding buyer perspectives.
A variety of tools are available in the market that makes data mining easier. While you may find more sophisticated features in paid data mining tools, some free platforms are also available which give satisfactory performance. This article discusses some of the top data mining tools that companies prefer using. So, let’s dive in!
Top Data Mining Tools
Developed by Megaputer Intelligence, PolyAnalyst is the leading system for extracting actionable insights hidden in piles of free text and structured data. It has become a tool of choice for companies that want to turn their data into meaningful business insight. The tool can access nearly any source of data and merge data sets from various sources. You can choose from a wide selection of statistical and machine learning algorithms and replete with a number of cutting-edge natural language processing (NLP) tools.
ELKI is an open-source data mining tool that is written in Java. It is designed to be easy to extend for students and researchers in this domain. ELKI offers data index structures in order to achieve high performance and scalability. In this tool, data mining algorithms and data management tasks are separated and enable independent evaluation. The tool is also open to arbitrary data types, file formats, or distance or similarity measures.
SAS Enterprise Miner
The Enterprise Miner by SAS reveals valuable insights with powerful data mining software. It offers descriptive and predictive modeling that gives insights to drive better decision-making. Using the tool, clients can dramatically shorten model development time for data miners and statisticians. Business users and subject-matter experts with limited statistical skills can also generate their own models using SAS Rapid Predictive Modeler.
Python has already proved to be an efficient tool for data science tasks. Python has a range of libraries that support data mining tasks. NumPy, SciPy, matplotlib, mlpy, Orange, pandas, and Jupyter are some of the popular libraries for the purpose of data mining. Many prefer Python for cleaning data, organizing data, and applying machine learning algorithms. The tool allows performing various data mining techniques like linear and logistic regression, classification, cluster analysis, and outlier analysis.
It is a comprehensive data science platform with visual workflow design and full automation. It is a powerful data mining tool that allows the rapid building of predictive models. It is an all-in-one tool that features hundreds of data preparation and machine learning algorithms to support data mining projects. It helps organizations take data mining to the next level and drive real business impact. Using the tool, one can also speed up and automate the creation of predictive models.
Related to data mining, Alteryx has a tagline ‘Deliver insights quickly, easily, and intuitively.’ The tool allows users of all skill levels to leverage clustering, classifications, and other data mining techniques to deliver meaningful insights. It slashes the data preparation time required for merging, cleansing, reshaping, and restructuring data sets to feed data mining algorithms. Drag and drop data mining tools make it simple to apply intelligence to data, enrich it, and route it for analysis.
DataMelt or DMelt is a free software used to perform numeric computation, mathematics, statistics, data analysis, symbolic calculations, and data visualization. The software combines the simplicity of scripting languages like Python, Groovy, and Ruby with the power of hundreds of Java packages. It is a portable application that needs no installation and is open source. The tool creates high-quality vector-graphics images that can be included in LaTeX and other text-processing systems.
Qlik’s big data management and analytics platform is an end-to-end solution that allows organizations to turn their huge datasets into actionable insights. Its real-time data integration and analytics platform allow data to be put into a governed enterprise catalog so that clients can ‘shop’ for the data they require without compromising their security policies. Qlik supports major BI use cases – from data discovery to visualization and executive reporting.
Now that you are aware of the top choices when selecting the best data mining tool, choose what suits best for your company.