What Are Data Mining Tools?
Data mining tools are advanced data analytics solutions that help users find hidden relationships and patterns in large data sets that other types of analysis might miss. Data mining platforms combine artificial intelligence (AI), machine learning (ML), and statistical analysis to identify data trends. The data mining process can be used to spot customer needs, find ways to boost revenue and profitability, engage more effectively with audiences, and derive industry-specific insights.
These days, data mining techniques and tools are more powerful than ever. Many data mining tools can now take advantage of abundant computing power and memory to crunch numbers and data with more speed and accuracy. This evolution of data mining tools is particularly important as more companies are processing big data for various digital transformation projects.
Here are some popular data mining tools:
- RapidMiner
- KNIME
- IBM SPSS Modeler
- Weka
- Orange
- Apache Mahout
- Alteryx Designer Cloud
- Talend Data Fabric
- Datamelt
- TIBCO Data Science
1. Rapid Miner
RapidMiner is a Data Mining application that is free to use. It is used for preparing results, learning machines, and implementing models. It has a range of products for the construction of new data mining applications and automated system research.
Key Features
- Support multiple data management approaches
- GUI or batch processing Integrates open, collaborative dashboards
- Big Data predictive analytics
- Remote data collection, joining, mixing, and integration
- Create, train, and test predictive models
- Reports and alerts activated
2. KNIME
The Konstanz Information Miner — better known as KNIME — is an open-source data analytics, reporting, and integration platform that requires minimal programming knowledge to use. It integrates machine learning and data mining components through modular data pipelining.
The KNIME Analytics Platform can be used for data wrangling, data modeling and visualization, spreadsheet automation, ETL, and a variety of other data preparation and mining processes. At its most basic level, KNIME is a free tool that users can download directly from the KNIME website. The Community Hub and Business Hub versions offer additional features for a higher price.
Key Features
- An active community is continuously integrating new developments.
- Workflow and component sharing and collaboration.
- Versioning and read access for unlicensed users.
- User-defined virtual cores for workflow execution.
- Advanced automation, deployment, and management features are available in paid plans.
3. IBM SPSS Modeler
IBM SPSS Modeler is a visual data science and machine learning tool that speeds up operational tasks for data scientists. This IBM solution has many use cases, including data discovery, data preparation, model management and deployment, and machine learning for data asset monetization.
SPSS Modeler is available on its own and in conjunction with IBM Cloud Pak for Data, which is a containerized data and AI platform for building and running predictive models on public clouds, private clouds, 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-the-box machine learning algorithms.
- Apache Spark integration to support faster in-memory computing.
- Enterprise-level data security and governance.
- Open-source compatibility with R and Python.
4. Weka
Also known as Waikato Environment is a machine learning software developed at the University of Waikato in New Zealand. It is best suited for data analysis and predictive modeling. It contains algorithms and visualization tools that support machine learning. Weka has a GUI that facilitates easy access to all its features. It is written in JAVA programming language. Weka supports major data mining tasks including data mining, processing, visualization, regression, etc. It works on the assumption that data is available in the form of a flat file. Weka can provide access to SQL Databases through database connectivity and can further process the data/results returned by the query.
5. Orange
Orange is an open-source data mining solution that includes advanced machine learning and data visualization capabilities. It helps users to more easily build visual data analysis workflows with a large toolbox of features.
Some of the visuals that Orange offers include box and scatter plots, decision trees, heatmaps, linear projections, and hierarchical clusters. With its many visualization options and training widgets, Orange is one of the most commonly used data mining and analytics tools in schools, universities, and online training courses for users who are new to data science.
Key Features
- Data visualization options include statistical distributions, box plots and scatter plots, decision trees, hierarchical clustering, heatmaps, and linear projections.
- Attribute ranking and selections.
- Data analysis workflow prototyping.
- Compatible with third-party data sources.
- Natural language processing, text mining, and association rules mining.
6. Apache Mahout
One of the best open-source data mining tools on the market, Apache Mahout, developed by the Apache Foundation, primarily focuses on collaborative filtering, clustering, and classification of data. Written in the object-oriented, class-based programming language JAVA, Apache Mahout incorporates useful JAVA libraries that help data professionals perform diverse mathematical operations, including statistics and linear algebra.
Key Features
- Versatile programming environment
- Pre-built algorithms
- Scope for mathematical analysis
- The Graphics Processing Unit (GPU) measures performance improvement
7. Alteryx Designer Cloud
Alteryx is known for its various data science and analytics automation solutions. The Alteryx Analytics Cloud Platform comes in multiple different versions, but it’s the Alteryx Designer Cloud that offers the best features and functions for most enterprise data mining requirements.
Many users select Alteryx Designer Cloud for its balance of sophisticated enterprise tools with intuitive visualizations and other usability features. Although it could run into some processing or memory trouble with the largest of data sets, its smart data samples, pushdown processing, and compatibility with various cloud and data warehousing environments make it possible for users to scale this tool as their needs grow.
Key Features
- Easy-to-use, drag-and-drop interface.
- No-code/low-code, cloud environment.
- Features for data prep, blending, and analysis.
- Project sharing, version control, collaboration workflows, and other collaboration features.
- Built-in governance and security features.
- Smart data samples and pushdown processing.Compatibility with AWS, Google Cloud Platform, and Snowflake.
8. Talend Data Fabric
Talend Data Fabric is a single, cloud-based platform that centralizes data integration, data quality and integrity management, data governance, delivery, and application and API integration. It is uniquely designed to consolidate data activities, providing intelligence and collaboration capabilities that complement data workers of various technical expertise levels.
Although the data integration portion of Talend Data Fabric is where most of the platform’s data mining functionality lies, the platform works best when all of its features are used in tandem.
Key Features
- 1,000+ built-in connectors and components for leading SaaS and on-prem applications, including Marketo, Workday, Salesforce, SAP, and ServiceNow.
- Application and API integration for microservices.
- Compatible with the following database and storage systems and providers: AWS, Azure, Google Cloud, Snowflake, Microsoft SQL Server, Oracle, Greenplum, SAS, Sybase, and Teradata.
- Compatible with big data platforms like Cloudera, Databricks, Google Dataproc, AWS EMR, and Azure HDInsight.
- Native Spark streaming to support real-time big data messaging systems.
9. Datamelt
DataMelt is a free tool for computational computation, engineering, data analysis, and data visualization. This program offers you the versatility of scripting languages, with the strength of hundreds of Java modules, including Python, Ruby, and Groovy.
Features:
- DataMelt offers data, a considerable volume of research, and statistical visualization.
- You can use this on various operating systems of different programming languages.
- It allows you to create high-quality images with vector graphics (EPS, SVG, PDF, etc.) that can be included in LaTeX and other text processors.
- Data Melt allows the use of scripting languages that are significantly faster than the C-implemented Python standard.
10. TIBCO Data Science
TIBCO Data Science is a unified data science solution that combines the strengths of TIBCO Statistica, TIBCO Spotfire Data Science, TIBCO Spotfire Statistics Services, and TIBCO Enterprise Runtime for R. Though the platform includes many advanced features, the interface is designed to be simple with a drag-and-drop setup and simple, Slack-like collaboration features.
TIBCO Data Science users can benefit from the tool’s pre-built templates, version control, and a variety of third-party integrations. A particular strength of this software is its variety and depth of data and workflow visualizations.
Key Features
- Team Studio for collaborative data pipeline creation.
- Drag-and-drop interface.
- Code integration through Jupyter Notebook.
- Integration opportunities with Python and R.
- User-created parameterized workspaces.
- Model management, scoring, and governance.
- Data science workload federation across SAS, MatLab, R, and Python.
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