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 use of data mining tools is a core practice of digital transformation.
Also see: Best Data Analytics Tools
Success in using data mining tools 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?
- What are Data Mining Tools?
- Best Data Mining Tools and Software
- SAS Visual Data Mining and Machine Learning
- Oracle Machine Learning on Autonomous Database
- Talend Data Fabric
- RapidMiner
- IBM SPSS Modeler
- Knime
- Orange
- Qlik
Also see: Top Data Visualization Tools
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. Using data mining tools often requires data visualization and business intelligence techniques.
These days, data mining is more powerful than ever. It can certainly perform text mining, but it is capable of far more sophisticated knowledge discovery techniques. Data mining can now take advantage of abundant compute power, and memory to crunch numbers and data rapidly and with more accuracy.
Also see: Data Mining Techniques
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.
Data mining 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 provides 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. In many data mining techniques, data visualization plays a core role. Text mining might be employed.
Also see: Top Business Intelligence Software
Best Data Mining Tools and Software
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.
Key Features
- Access, profile, cleanse and transform data with self-service data preparation capabilities with embedded AI. Can combine unstructured and structured data in integrated machine learning programs.
- Best practices templates enable a consistent start to building models. Analytical capabilities include clustering, regression, random forest, gradient boosting models, support vector machines, natural language processing, topic detection.
- Users can visually explore data and create and share visualizations and interactive reports.
- Network algorithms explore the structure of networks – social, financial, telco and others.
- Modelers and data scientists can access SAS capabilities from their preferred coding environment – Python, R, Java or Lua.
- Includes access to a public API for automated modeling; or use an API to build and deploy custom predictive modeling applications.
Pros
- Automatically generate insights, including summary reports about a project and champion and challenger models. Simple language from embedded natural language generation facilitates report interpretation and reduces the learning curve.
- Automated feature engineering selects the best set of features for modeling by ranking them to indicate their importance in transforming data.
- Generative adversarial networks (GANs) generates synthetic data, both image and tabular, for deep learning models.
- Scalable in-memory analytical processing provides concurrent access to data in memory in a secure, multiuser environment and distributes data and analytical workload operations across nodes – in parallel – multithreaded on each node for very fast speeds.
Cons
- As the big name in analytics, SAS is typically more expensive than other tools.
- There are a great many tools and sub-tools within the SAS ecosystem. Great for data scientists and analytics experts, but it can sometimes be challenging for the less skilled.
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.
Key Features
- Integrated notebook environment supports SQL, PL/SQL, Python, and markdown interpreters, where the same notebook can contain SQL and Python paragraphs – allowing users to choose the most effective language for the task– and users can version notebooks and schedule notebooks to run.
- Automated machine learning (AutoML) from a Python API (OML4Py) and no-code user interface (OML AutoML UI).
- Python API (OML4Py) for scalable data preparation and exploration, and model building, evaluation, and scoring.
- Store Python scripts and objects in the database for unified security, backup, and recovery, and use with embedded Python execution.
- Run user-defined Python functions in database spawned and controlled Python engines (embedded Python execution), with built-in data-parallel and task-parallel features.
- Deploy in-database and third-party ONNX format models for real-time scoring via a RESTful service for model management and deployment.
- Deploy models from AutoML UI directly to OML Services.
Pros
- Minimize or eliminate data movement for Oracle Autonomous Database data.
- Score data using in-database models with integrated SQL prediction operators in SQL queries.
- Data and model governance via Oracle Autonomous Database security models in development and production.
- On-premises and cloud availability for ML capabilities.
- Oracle tools integration, including Oracle Analytics Cloud, Oracle Streaming Analytics, and Oracle APEX.
Cons
- Use cases requiring GPU compute, such as deep learning image CNNs, are not supported.
- OML Notebooks, OML AutoML UI, and OML Services are available on Oracle Autonomous Database – Shared only.
- Solution is optimized for data residing in Oracle Autonomous Database so it is best for this platform.
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.
Key Features
- 1,000+ built in connectors and components to leading SaaS and on-prem applications, including: Marketo, Workday, Salesforce.com, SAP, ServiceNow.
- Data quality, preparation, and governance in a unified platform.
- Application and API integration for microservices.
- Supports most databases and storage including: AWS, Azure, Google Cloud, Snowflake, Microsoft SQL Server, Oracle, Greenplum, SAS, Sybase, Teradata; and big data platforms including: Cloudera, Databricks, Google Dataproc, AWS EMR, Azure HDInsight.
- Native Spark streaming to support real-time big data messaging systems.
Pros
- Talend Data Quality Service scales the use of healthy data using automated frameworks to establish a data quality framework.
- Ready-to-use dashboards, ongoing monitoring and reporting.
- Trust Score for Snowflake: the only solution that profiles entire datasets inside Snowflake Data Cloud using native Snowflake processing to ensure data professionals can assess quality at scale for healthy, analytics-ready data.
- Self-service data APIs make creating and operationalizing compliant, no-code APIs happen fast.
Cons
- Those without Java expertise may find it challenging.
- The learning curve can be steep.
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
- Finds patterns in text, flat files, databases, data warehouses, and Hadoop distributions in a multi-cloud environment.
- 40+ out-of-box machine learning algorithms.
- Integrate with Apache Spark for fast in-memory computing.
- Speed data analysis within-database performance and minimized data movement.
Pros
- Takes advantage of open source-based tools such as R and Python.
- Empowers data scientists of all skills, programmatic and visual.
- Facilitates a hybrid approach — on-premises and in the public or private cloud.
- Start small and scale to an enterprise-wide, governed approach.
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.
Key Features
- Perform simple data analysis with data visualization.
- Explore statistical distributions, box plots and scatter plots, or dive deeper with decision trees, hierarchical clustering, heatmaps, and linear projections.
- Interactive data exploration for rapid qualitative analysis.
Pros
- Focus on exploratory data analysis instead of coding.
- Defaults make fast prototyping of a data analysis workflow easy.
- Easy to learn so is used at schools, universities and in professional training courses.
Cons
- Advanced analysis can be challenging for some users.
- Graphics could be improved.
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.
Key Features
- Create a data literate workforce with AI-powered analytics.
- Insight Advisor, an AI assistant in Qlik Sense, offers insight generation, task automation, and search & natural-language interaction.
- Available as SaaS or a choice of multicloud or on-premises.
- Associative Engine allows people to explore in any direction.
- Combine and load data, create smart visualizations, and drag and drop to build analytics apps.
Pros
- Insight Advisor gives suggested insights and analyses, automation of tasks, search and natural language interaction, and real-time advanced analytics.
- Interactive mobile analytics.
- Embedded Analytics.
Cons
- Basic users may struggle to learn it at first.
