Commercial Data Mining Tools

These days, businesses have a wide range of tools at their disposal to use business intelligence software to transform raw data into actionable next actions. Some data mining technologies use machine learning techniques to accelerate this process. Modern data mining goes beyond basic analysis to more efficiently and effectively extract usable information from massive data volumes.

Top 5 Data Mining tools in the Market

RapidMiner Studio

Data preparation, blending, visualization, and exploration are made easier using RapidMiner Studio, a visual data science workflow builder. Its predictive modeling and data mining initiatives are powered by machine-learning techniques

Features

Visual Workflow Analytics. − The system offers a drag-and-drop interface for creating analytics processes. This user-friendly user interface makes modeling quick and simple.

Connectivity and Management − Both structured and unstructured data may be accessed, loaded, and analyzed by users.

Processing − Structured and unstructured data may be combined in the solution, which can use recently created datasets for analysis.

Data visualization − Users have access to a wide range of data visualization tools, such as distribution plots, transition matrices and graphs, charts, and statistical models.

Modeling − The platform is capable of doing both predictive modeling and model validation thanks to a variety of modeling capabilities and machine learning algorithms.

Alteryx Designer

A self-service data science application called Alteryx Designer completes essential data mining and analytics activities. With the help of built-in drag-and-drop tools, users may combine and prepare data from diverse sources and develop repeatable processes.

Features

Connectivity − Alteryx Designer can connect to a variety of sources with native data connections to more than 70 sources, including data warehouses, ERP and cloud-based systems, common files, Microsoft Office files, social network data, and more.

Preparation and Blending − Alteryx Designer's visual user interface assists users in maximizing value through data extraction and purification, evaluating the accuracy and completeness of data sets before analysis.

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Analytics and modeling − With access to hundreds of analytics apps through the Alteryx Analytics Gallery, Alteryx Designer has the full range of data analysis covered, from spatial analytics to predictive analytics and beyond.

Processes − Users may develop repeatable, automated workflows that produce analytics models and reports using a visual, drag-and-drop interface that requires no coding.

Reporting Options − The solution's insights can be converted into reports that can be refreshed on-demand or exported to a number of different file types, such as spreadsheets, XML, PDF, etc.

Sisense for Cloud Data Teams

Sisense for Cloud Data Teams, formerly known as Periscope Data, is an analytics tool that enables users to extract useful insights from cloud-based data.

Features

Connectivity − The system enables users to enhance their dashboards with data from a variety of files, databases, drivers, applications, and services through an ecosystem of native data connectors and ETL collaborations.

Engine − The Sisense engine ingests and analyses data where it is in its infrastructure, such as its warehouse, leading to optimum query performance and large-scale ingestion.

Cloud Data Pipelines − The engine gives customers visibility and control over their pipelines with a flexible, low-maintenance solution. Users may decide when and how often their data is refreshed as well as how the information flow appears.

Machine Learning − Utilizing datasets from their database to train machine learning models, users may test those models on unstructured data with Sisense for Cloud Data Teams.

Real-Time Modeling − Using the "Model-as-You-Go" methodology, users may do ad hoc analysis on both modeled and raw data without first creating models.

TIBCO Data Science

To operationalize machine learning throughout an enterprise, TIBCO Data Science is a data mining solution that integrates the capabilities of many big data analytics and statistical packages.

Features

Full Spectrum of Analytics − With over 16,000 advanced analytics functions and a large selection of machine learning, predictive, and text analytics, the platform enables companies to model, transform, and use their big data in a variety of ways.

Discovery and Management − The solution can automatically index metadata about projects and do analyses without transferring data thanks to native access to the majority of sources, including Apache Hadoop, Spark, Hive, and relational databases.

Machine Learning − Automated analytics models can improve performance through iterative learning from data thanks to machine learning.

In-Cluster Processing − When a user runs a process, the solution automatically optimizes and pushes calculations to a number of database systems so that analysts may run their algorithms at scale without having to move the data or modify their algorithms in accordance with the logic of the databases.

Visual Drag-and-Drop Interface − Users of various ability levels may query using the visual drag-and-drop interface without needing to be familiar with SQL queries or programming languages.

5)SAS Visual Data Mining and Machine Learning?

A multimodal platform for predictive analytics and machine learning, SAS Visual Data Mining and Machine Learning offer end-to-end data mining with both a full visual and programmatic interface.

Features

Preparation − An extensive data profile of input sources may be carried out by the solution using distributed data management methods, and intelligent recommendations for variable measurement and role can be made.

Drag-and-Drop Interface − Although coding is still an option, SAS VDMML provides an interactive drag-and-drop interface that does not need it.

Automated Modeling − By evaluating the features to show their significance in changing the data, the system automatically proposes the optimal sets of characteristics for modeling.

Model Evaluation and Scoring − SAS Visual Data Mining and Machine Learning automatically computes statistics for the performance of supervised learning models and creates SAS DATA step code for model evaluation that may be used with training, holdout, and fresh data.

Automated Insights − By generating insights and reports regarding projects and models automatically, the technology lowers the learning curve for business analysts.