Best Data Mining Tools & Software | eWEEK

Data mining tools are enjoying a dramatic increase in interest, due to data trends driving today’s businesses. Clearly, data analytics is now firmly embraced by businesses of all shapes and sizes, and data mining is a core practice of digital transformation.

Success in data mining is all about two factors:

First, it’s about which data mining techniques you use to extract meaningful insights from a vast ocean of data. This is accomplished by gathering and prepping raw data from innumerable sources and subjecting them to algorithms and analysis to find patterns and common elements. Additionally, it’s about which data mining tools you use. To be sure, there’s an enormous amount of variety in data mining tools. So let’s dive in.

What is Data Mining?

Data mining is classified as an advanced data analysis technique. It finds the hidden relationships and patterns that other types of analysis might miss. It incorporates artificial intelligence (AI) and machine learning to spot customer needs, find ways to boost revenue and profitability, and engage more effectively with audiences.

These days, data mining is more powerful than ever. It can now take advantage of abundant compute power, and memory to crunch numbers and data rapidly and with more accuracy.

What are Data Mining Tools?

Data mining tools can be deployed on-premises on in the cloud. Some are offered as traditional software, some are open source, and many exist as software as a service (SaaS) solutions.

These tools use machine learning algorithms and statistical models to make sense of massive data sets. Whether it is social media platforms, CRM systems, website analytic tools, mobile applications, organizational databases, or other enterprise systems, data mining software helps make decisions smarter, and provide better data on which to base strategy.

Not all tools use the same approach. Some of the data mining techniques used are descriptive analytics, cluster analysis, rule learning, classification, predictive analytics, regression analysis, forecasting, and risk assessment. Some tools favor one approach. Others combine several.

Top Data Mining Tools

eWeek evaluated many different data mining tools. Here are our top picks, in no particular order:

SAS Visual Data Mining and Machine Learning

SAS Visual Data Mining and Machine Learning (VDMML) is a comprehensive visual – and programming – interface that supports the end-to-end data mining and machine learning process. SAS VDMML, which runs in SAS Viya, combines data wrangling, exploration, feature engineering, and modern statistical, data mining, and machine learning techniques in a single, scalable in-memory processing environment.

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Oracle Machine Learning on Autonomous Database

Oracle Machine Learning on Autonomous Database uses more than 30 in-database scalable machine learning algorithms accessible from SQL and Python APIs (including OML4SQL and OML4Py). It supports classification, regression, clustering, association rules, feature extraction, time series, anomaly detection, among other machine learning techniques.

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Talend Data Fabric

Talend Data Fabric is a single, unified platform that centralizes data integration, quality, governance and delivery. It is unique in that it is designed to consolidate data activities, providing intelligence and collaboration capabilities to meet data workers at their technical level, in a cloud-based platform.

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RapidMiner

RapidMiner is a business analytics workbench with a focus on data mining, text mining, and predictive analytics. It uses a wide variety of descriptive and predictive techniques to give the insight to make profitable decisions. RapidMiner, together with its analytical server RapidAnalytics, also offers full reporting and dashboard capabilities.

Key Features

  • Instead of holding complete data sets in the memory, only parts of the data are taken through an analysis process and the results are aggregated in a suitable location later on.
  • Fast performance as it takes the algorithms to the data instead of the other way around.
  • Graphical connection of Hadoop for the handling of big data analytics.
  • Meta data propagation to eliminate trial and error.
  • RapidMiner can continually observe the storage and runtime behavior of analysis processes in the background and identify possible bottlenecks.

Pros

  • No software license fees.
  • Flexible/affordable support options.
  • Fast development of complex data mining processes.
  • Installation takes less than 5 min.

Cons

  • Can be a steep learning curve.

IBM SPSS Modeler

IBM SPSS Modeler is a visual data science and machine learning solution designed to speed up operational tasks for data scientists. Organizations use it for data preparation and discovery, predictive analytics, model management and deployment, and machine learning to monetize data assets.

SPSS Modeler is also available within IBM Cloud Pak for Data, which is a containerized data and AI platform that lets you build and run predictive models on cloud and on-premises.

Key Features

Pros

Cons

  • Can be expensive.
  • Customization can be challenging.

Knime

The Konstanz Information Miner or KNIME  is an open-source data analytics, reporting, and integration platform. It integrates various components for machine learning and data mining through modular data pipelining based on a building-block approach.

Key Features

  • KNIME Analytics Platform is open source software for data science and data mining.
  • An active community is continuously integrating new developments.
  • KNIME attempts to make understanding data and designing data science workflows and reusable components accessible to everyone.
  • KNIME Server is for team-based collaboration, automation, management, and deployment of data science workflows as analytical applications and services.

Pros

  • Non experts are given access to data science via KNIME WebPortal or can use REST APIs.
  • Drag and drop style interface without the need for coding.
  • Models each step of a data analysis, controls the flow of data, and ensures work is current.
  • Blend tools from different domains with KNIME native nodes in a single workflow, including scripting in R and Python, ML, and connectors to Spark.

Cons

  • Interface is a little clunky.
  • Can hog memory resources.

Orange

Orange is an open-source machine learning and data visualization tool. It helps to build data analysis workflows visually, and comes with large toolbox. 

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Qlik

Qlik Sense is a data analytics and data mining platform that includes an associative analytics engine, AI capabilities, and operates in a high-performance cloud platform. It empowers executives, decision-makers, analysts, and anyone else with BI that users can freely search and explore to uncover insights.

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