Choosing the Best Data Mining Software

These days, the quantity of data that humans collect is so vast that it would be impossible to sort through it manually. To help us make sense of all the information available, data scientists have developed powerful data mining software that effortlessly sorts facts using a variety of complex methods. These include data analysis tools that use specialized algorithms paired with artificial intelligence (AI) and machine learning systems to gather the most accurate and useful information rapidly.¹

Predictive analytics is one of the most significant tools to emerge as the result of modern data mining techniques. It gives companies the ability to make accurate predictions using a combination of current statistics and historical data, often modeled with the help of AI and machine learning.²

Typically, this involves using powerful computers to analyze huge amounts of data, conducting billions of calculations per second and finding patterns that would take humans years or decades to uncover. Weather forecasters, financial analysts, insurance firms and marketers use predictive analytics to make more informed decisions about upcoming events and future behavior.³

Read on to explore the uses of data mining and several of the best pieces of data mining software currently available.

Data mining tools are used to find patterns, statistics, results and other beneficial information within large, unfiltered data sets known as big data. Companies use them to turn raw data into useful, comprehensive information which can then be used to guide business decisions.⁴

Data mining software is primarily used to assist in data analysis. The most common instances of data mining in day-to-day life include:⁵

• Data gathered by email software providers to filter out spam
• Information gathered by social media sites to channel advertisements more effectively
• Sales and marketing statistics gathered by retailers to target customers more effectively
• Information gathered by credit scoring companies to make accurate risk assessments

Some of these goals, such as spam filtering, can be achieved relatively easily using just a few pieces of readily available data. Others, such as credit scoring, require complex algorithms to sort—accurately—massive amounts of mismatched data collected from disparate sources.⁶

Companies that use data mining software typically collate all their data into a singular virtual 'warehouse.' This makes it easier for data scientists to develop automated programs that persistently validate against new warehouse data to provide the most updated and accurate information.⁴

The vast majority of data mining software is written in the programming languages Python and R. Inexperienced coders, however, can also access the power of data analysis using one of the many graphical user interfaces (GUIs) available.⁷

Oracle Data Miner is a GUI included in the Advanced Analytics component of one of the world's most popular database providers. Features include algorithms for classification, prediction, regression and associations plus extras like specialized analytics and anomaly detection.⁸ For companies that already use Oracle software, Oracle Data Miner will work seamlessly with pre-existing Oracle databases, making the initial installation and setup easier.

IBM's SPSS Modeler is a highly advanced analytics platform used by data scientists for largescale initiatives. It's one of the world's leading GUI-based machine learning and visualization solutions, with enterprise-class security and high scalability.⁹

RapidMiner is one of the most popular 'no-code' data mining programs available, due largely to its easy-to-use interface and open-source code. Its simple interface hides a powerful back end, with features that include text mining, data preparation, machine learning and predictive modeling.¹⁰

KNIME is a free and open-source data analytics platform developed for research and collaboration by German software engineers at the University of Konstanz. The modular system allows for easy integration of external datasets and additional plugins, with simple data visualizations and process execution.¹¹

Orange is another open-source data mining toolkit that provides a wealth of features, from machine learning to interactive visualizations. Written in Python, the component-based system uses workflows and can have extra functionalities added to its base widget set (data, visualize, classify, regression, evaluate and unsupervised).¹²

Predictive analysis holds exceptional promise for the future. With an expected growth of 24.5% between 2019 and 2026, the demand for experts in this dynamic field is likely to remain high.¹³

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Sources:

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3. Retrieved on October 12, 2021, from investopedia.com/terms/p/predictive-analytics.asp
4. Retrieved on October 12, 2021, from investopedia.com/terms/d/datamining.asp
5. Retrieved on October 12, 2021, from iberdrola.com/innovation/data-mining-definition-examples-and-applications
6. Retrieved on October 12, 2021, from worldscientific.com/doi/abs/10.1142/S0218194020500072
7. Retrieved on October 12, 2021, from springboard.com/library/data-science/data-mining/
8. Retrieved on October 12, 2021, from predictiveanalyticstoday.com/oracle-data-mining-odm/
9. Retrieved on October 12, 2021, from ibm.com/products/spss-modeler
10. Retrieved on October 12, 2021, from analyticsvidhya.com/blog/2021/10/intro-to-rapidminer-a-no-code-development-platform-for-data-mining-with-case-study/
11. Retrieved on October 12, 2021, from knime.com/knime-open-source-story
12. Retrieved on October 12, 2021, from orangedatamining.com/workflows/
13. Retrieved on October 12, 2021, from globenewswire.com/news-release/2021/03/18/2195402/0/en/At-24-5-CAGR-Global-Predictive-Analytics-Market-Size-to-Register-Record-Value-of-USD-5-7-Billion-by-2026-Says-Facts-Factors.html</sup>