Data is at the crux of some of the biggest decisions modern businesses take, and data mining is an effective technique to help you do that.

Every business deals with an overwhelming volume of data, which when used the right way, can bring a lot of benefits to your organization.

This is where data mining is useful. 

It can help businesses optimize their operational efficiency, reduce costs, and make informed decisions.

And you can perform data mining efficiently using data mining software. It will help accelerate the process and save you time that you can dedicate to utilizing the gained data.

Let’s talk about data mining more and the best data mining software you can try.

What is Data Mining?

Data mining refers to a process of data searching, extracting, and evaluation. Data can be textual graphic patterns such as calligraphy, literary and language figures, statistics, etc. 

Data mining originated from the field of computational linguistics in the subfield of computer science, linguistics, art sciences, and mathematical statistics.

It aims to extract data using computer programs, analyses, and intelligent methods from data sets, document the analysis results and restructure this information to be able to take meaningful insights.

Apart from text analysis, data mining also involves data management, database management, and database engineering. Data management begins with data pre-processing, creating data models, and data processing with strict and non-strict statistical inferences.

Now, let’s talk about some of the best data mining software.

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RapidMiner Studio

Get a comprehensive data mining platform with full automation and visual workflow design from RapidMiner Studio. It helps automate and speed up the predictive model creation process with the help of a drag-and-drop visual interface. 

You will get more than 1500 functions and algorithms that ensure the best model in every use case. RapidMiner Studio offers pre-built templates for predictive maintenance, customer churn, fraud detection, and more. 

RapidMiner lets you create point-and-click connections to enterprise data warehouses, cloud storage, social media, business applications, data lakes, and databases. Beginners will also find proactive recommendations in each step to proceed.

Run ETL and data prep inside the database to maintain optimized data for analytics. Understand trends, distributions, and patterns with histograms, parallel coordinates, line charts, box plots, scatter plots, etc., to fix data quality problems quickly, including missing information and outliers.

Eliminate the hard work during data preparation with RapidMiner Turbo Prep and create impactful and valuable machine learning models quickly without writing a single line of code. It will reveal the real performance of the model before going to production. 

Furthermore, create easy-to-explain and easy-to-understand visual data mining workflows and also deploy code-containing and code-based models into the platform. 

Integrate RapidMiner with existing applications like Python and R. Download the latest functionality provided by the community and add new capabilities via its extension mechanism. 

Teradata

Experience data, insights, and results with Teradata Vantage. It is a connected multi-cloud platform that unifies everything for enterprise analytics. 

Teradata helps drive your business forward by allowing enterprise data analytics ecosystem, predictive intelligence, and delivering actionable answers. It offers a hybrid approach to satisfy the demands of a modern enterprise.

This multi-cloud platform gives you the portability and flexibility to deploy anywhere, such as on-premises and public clouds (Azure, AWS, Google Cloud). Teradata’s expert teams can help you leverage the data to optimize your business operations and achieve stunning value. 

Query your inventory with Teradata in real-time and make sure everything is working and running fine without worrying about uptime. In addition, Teradata Vantage delivers countless intelligence to help build a next-generation business. 

Furthermore, its multidimensional and enterprise-grade scalability allows you to scale the dimensions to handle your massive data workloads. Advance your artificial intelligence and machine learning to power your models with better results and quality. 

Give role-based and secure, no-code software to your teams to draw 100% data that can support the key goals of your business. It also supports all data formats and types, such as BSON, Avro, CSV, Parquet, XML, and JSON.

Teradata Vantage will not surprise you with additional costs. The intuitive console allows you to track your resource usage easily so that you know what you are paying for. 

Oracle Data Miner

Oracle Data Miner enables businesses, data analysts, and data scientists to view data and work directly inside the database by using a simple drag-and-drop workflow editor. 

Oracle Data Miner is an extension to Oracle SQL Developer that documents and captures the graphical analytical workflow steps that users are taking to explore data. In addition, its workflow is simple and useful for executing analytical methodologies and sharing insights.

This platform generates PL/SQL and SQL scripts and quickly offers an API to accelerate model deployment across the enterprise. You will also get an interactive workflow tool to create, evaluate, modify, share, and deploy machine learning methodologies. 

Furthermore, you will get graph nodes to view data, such as summary statistics, box plots, scatterplots, and histograms. Various nodes, like transform, column filter, and model build nodes, help you drive your business. 

Oracle Data Miner can minimize the time between model development and deployment by eliminating data movement and preserving security. It will also empower your teams by helping them develop a diverse skill set using machine learning algorithms. 

KNIME

Create and produce data mining with KNIME that offers end-to-end data science support for your business and enhances productivity. 

You will get two complementary tools with one enterprise-grade platform. You will also get KNIME Analytics, which is an open-source platform to create and deploy commercial KNIME server and data science models.

Moreover, KNIME is open, intuitive, and can integrate new developments continuously to understand and design data science workflows that are accessible to everyone. KNIME server is useful for team collaboration, management, deployment, and automation. 

If you are not an expert, KNIME offers access to the KNIME web portal. Many Extensions are designed by KNIME itself so that you can do something more. Its community and partners also offer extensions. KNIME integrates with open-source projects so that you will never lack anything. 

KNIME Analytics Platform is available on Amazon AWS and Microsoft Azure. KNIME can help you access, transform, and merge all the data and analyse it using your preferred tools. It will support your business with wide data mining practices and useful insights collected from the data.

Download KNIME now and start building your first workflow.

Orange

Data mining is now fun with Orange which provides open-source data visualization and machine learning. It offers a diverse toolbox to create data analysis workflows easily and visually. 

You can perform simple data visualization and analysis and explore box plots, scatter plots, statistical distributions, etc. Orange allows you to go deeper with hierarchical clustering, heatmaps, decision tree, linear projections, and MDS. 

Orange can convert multidimensional data into 2D visualization with better attribute selections and rankings. You will also find a graphical user interface to focus more on data analysis rather than wasting time in coding. 

Universities, schools, and training courses across the globe use Orange for its awesome offerings. It supports visual illustrations and hands-on training of concepts from data mining. You will also get widgets to make your training even better.

Furthermore, use different add-ons to mine data from external sources, perform natural processing and text mining, carry out network analysis, infer item sets, and more. In addition, molecular biologists and bioinformaticians can use Orange to rank various genes through enrichment analysis and differential expression.

SAS

Reveal valuable insights with SAS Enterprise Miner – a robust data mining software for your business. It helps you streamline the whole process to develop quick models and understand the key relationships. 

SAS offers multiple tools to develop better models. Using a self-documenting and interactive process flow diagram, you can map the whole data mining process to extract better results. 

Subject matter experts and business users with limited skills can easily generate their own models via SAS Rapid Predictive Modeler. You can also enhance your prediction accuracy by comparing assessments and prediction statistics from the models created with various approaches.

SAS eliminates manual rewriting by allowing you to deploy the model automatically and generate scoring code for all the stages. It also offers an easy-to-handle GUI, batch processing, advanced predictions, descriptive modelling, high performance, open-source integration, cloud deployment option, scalable processing, and more.

Qlik

Qlik Intelligence Platforms can bridge the gap between insights, data, and action. It gives you AI-driven, collaborative, actionable, and real-time data and analytics visualization. 

Qlik accelerates ingestion, data replication, and streaming across various heterogeneous mainframe, SAP, SaaS, and database applications. You can automate ETL and design code generation along with ongoing updates.

The platform will help reduce the cost, risk, and time to deliver an agile cloud data warehouse. You can use push-down, and modern ELT approaches to convert, enrich, standardize, consolidate, and join data from heterogeneous structures.

Furthermore, Qlik’s no-code cloud-native service streamlines and automates your workflows between Qlik Sense and SaaS applications to recommend actions from the insights. You will also get easy-to-use dashboards with interactivity and full support for free-form exploration and search.

Qlik leverages AI to assist whole analytics, enabling more users to get extreme value from the data. With the help of open APIs, you have the chance to embed analytics into operational applications and create external applications.

If you spot any sudden change in the data, it will immediately prompt the relevant action. Qlik also offers flexible deployment options to protect local governance needs and data location with multiple cloud options. 

Togaware’s Rattle

Rattle is a graphical user interface for data science using R. It utilizes a GUI toolkit, i.e., RGtk2, which can be installed from the Microsoft CRAN repository. 

Know the capabilities of Rattle software, which also provides robust command-line utilization. It

  • Shows visual and statistical summaries of data
  • Transforms the same data for modelling
  • Builds unsupervised and supervised machine learning models 
  • Presents high-performance models graphically
  • Scores the latest datasets for deployment. 

All the interactions are captured as the R script, which is again executed in R independently with the Rattle interface. You can learn the tool and use it to develop your skill sets in R. It will further help you in building initial models with powerful options. 

Rattle is a free, open-source platform and its code is available in the Bitbucket git repository. You will have the freedom to review the code, use it for the purpose you want, and extend it.

Weka

Weka provides tools to implement various machine learning algorithms, process data, and visualize it. 

You can apply machine learning techniques to data mining problems in the real world. It follows simple steps:

  • You will get raw data from the field, which may contain various irrelevant fields and null values. 
  • Use the data preprocessing tools from Weka to clean the data. 
  • Save the cleaned data in the local storage to apply machine learning algorithms.
  • Depending on the machine learning type or model, you will select from the available options, including classify, cluster, or associate.
  • Automate the workflow

You have the freedom to select any algorithm provided by Weka and set your desired parameters to run the dataset. Get statistical output from Weka and a visualization tool for data inspection.

It applies various models on the same dataset to compare models’ outputs and select the best you need. 

Sisense

The API-first analytics platform, Sisense, delivers completely customizable and white-labeled analytics whenever you need it.

Transform your old-fashioned working style and grow your business by unleashing the power of data. Unlock data from on-premises and the cloud for data analysis and get better outcomes. 

You can automate the multi-step actions in your workflow and create custom experiences to accelerate the workflows. Sisense offers an open cloud platform that is extended through tech partnerships to enhance scalability.

Moreover, you can add AI-powered analytics into your workflows, applications, products, and processes to experience the intelligence in the right place at the right time to eliminate slow flow.

Whatever your skill level is, Sisense can empower everyone to infuse analytics effectively to make better business decisions. You can also differentiate the products, empower your consumers, and create new streams with AI-powered analytics. 

InetSoft

InetSoft‘s Style Intelligence makes analysing fast and easy. It is a web-based platform that accesses data from any source irrespective of the database size and handles small data sets for easier and quicker analysis.

This is one of the best data mining software for your business to weed through a wide range of data caches and get new market research tools.

Style Intelligence can handle big data projects and is designed using a proprietary data grid cache technology based on MapReduce principles that facilitate Big Data.

Apache Mahout

Apache Mahout is a mathematically expressive Scala DSL and distributed linear algebra framework specially designed for data scientists, statisticians, and mathematicians to implement their algorithms.

This is an open-source data science project that helps in creating machine learning algorithms. It has a lot of things going on at various levels. It implements popular learning techniques, including recommendation, classification, and clustering. 

Apache Mahout’s algorithms are written on Hadoop. Thus, it works well and uses the Hadoop library to scale in the cloud. You will get a ready-to-use and easy-to-use framework for your data mining tasks. It also allows applications to analyze Big Data quickly and effectively. 

H2O

Get the Gene Mutation AI that brings intelligent decisions directly to clinicians with H2O. It will help you track, manage, and predict COVID-19-related admissions in hospitals.

H2O solves many complex problems in your business and accelerates innovative ideas with actionable results. It can transform the way AI is built and consumed and has a built AI that makes the work faster and easier. 

Furthermore, H2O maintains speed, transparency, and accuracy so that you can build models without any limitations. Streamline your workflows according to the performance by monitoring the data to make a current decision.

With an intuitive AI AppStore, you can deliver innovative solutions easily to the end-users. Over 20,000 organizations use H2O for data mining technology. It can help optimize your operations by delivering actionable insights, streamlined operations, reduced risks, and personalized experiences. 

Start a 90-day free trial today and get hands-on experience with its AI cloud to build world-class applications and models in on-premises and the cloud. 

How does it work?

Data mining involves various processes, starting with understanding the business requirements as to why you need to extract data and use it. 

The process is divided into three main phases – data pre-processing, data mining, and results in validation.

Data pre-processing

Data pre-processing is necessary to understand variations in data sets before the actual mining can happen.

Since data mining can uncover useful patterns present in data sets, your target data must be massive enough to contain such patterns. In addition, this data set must be concise enough so that you can mine data within the required time frame.

So, before you begin to mine data, you must assemble a large volume of target data sets that you can source from a data warehouse. Next, you must clean that data to remove unnecessary fluff and missing Information.

Data Mining

After you have assembled the target data, the actual process of data mining starts. It involves six main steps – anomaly detection, dependency modeling, clustering, classification, regression, and summarization.

  • Anomaly detection: This involves identifying irregular datasets that can be useful or have some errors in them.
  • Dependency modeling: In this stage, the relationship between different variables is found. It is also known as association rule learning or market basket analysis.
  • Clustering: It involves discovering structures and groups in data sets that look similar.
  • Classification: This is classifying data based on certain parameters.
  • Regression: It discovers relationships between data sets or data in order to find such a function that can model the data with the least error possible.
  • Summarization: This is where you visualize data and generate reports to provide a compact, more meaningful representation of the extracted data.

Results Validation

This is the final step of knowledge discovery from collected data to verify the patterns generated in data mining. 

Not all the patterns discovered by data mining algorithms need to be valid. Hence, this step is crucial. It is done on a test data set where the discovered patterns are applied. Next, the resulting output is put into comparison with the desired output.

If the patterns meet the desired standards, the learned patterns are interpreted and turned into meaningful knowledge. But if it does not meet the standards, you must reevaluate the results by making the required changes in the pre-processing and data mining stages.

Why do you need data mining?

Data mining is useful for data analytics and business intelligence to help businesses gain deeper knowledge about their organization, customers, competitors, and industry. Some of the uses of data mining are:

  • Sales and marketing: Businesses collect information about their target customers in order to optimize their sales and marketing efforts along with products and services.
  • Education: Educational institutions can use data mining to extract student data and use it to improve the quality of education.
  • Fraud detection: SaaS businesses, banks, and other organizations can use data mining to observe anomalies in their security posture and prevent cyberattacks.
  • Operations: Businesses can use data mining to optimize their operations, reduce costs, and make informed decisions.

Conclusion

Data mining is an effective way to collect meaningful information and put it to your business use. It will help optimize your operations, and costs, and help you make better business decisions.

This, using the best data mining software and keep gaining wonderful insights for your business.