Data mining process helps to identify patterns and relationships in large data sets. It is an advanced analytics technique which combines machine learning and Artificial intelligence to get useful information which help businesses to know more about customer needs, improve revenues, reduce costs, enhance customer relationships etc.

In this article we will look at some data mining tools which made their place in the top 10 and shaped the IT and business landscape in the year 2021. Understand their strengths and features.

List of top Data Mining Tools

MonkeyLearn –

It is a machine learning platform which specializes in mining of text. It is available in a user-friendly interface and can be integrated with existing tools to perform real time data mining. It supports various data mining tasks such as detecting topics , sentiment, and intent to extract keywords and named entities. Text mining tools are used to automate ticket tagging and routing in customer support, detect negative feedback in social media, and fine grained insight which lead to better decision making.

RapidMiner –

It is a free open-source data science platform which has hundreds of algorithms for data preparation, machine learning, deep learning, text mining and predictive analysis. It is a drag drop interface and pre-built modules to allow non-programmers to create intuitive workflows for specific user cases like fraud detections and customer churn. RapidMiner studio helps to visualize results to spot patterns, outliers and data trends.

Oracle Data Mining –

It is a component of Oracle Advanced Analytics which enables data analysis to build and implement predictive models. It has several data mining algorithms for activities like classification, regression, anomaly detection and prediction. It helps to build models which help to predict customer behaviour, segmentation of customer profiles, detection of fraud, and targeted best prospects. Java API can be used by developers to integrate models to discover new trends and patterns in business intelligence applications.

IBM SPSS modeler –

It allows data scientists to visualize and speed up the data mining process. Advanced algorithms can be used without much programming experience to build predictive models using drag and drop interfaces. Data science teams can import vast amounts of data from various sources and arrange them to discover trends and patterns. Standard version of tool supports numerical data from spreadsheets and relational databases , for analytical capabilities premium version is required.

Weka –

It is an open-source machine learning application developed by University of Waikato in New Zealand written in JavaScript having a vast collection of data mining algorithms. Variety of data mining tasks such as pre-processing, classification, regression, clustering, visualisation in GUI interface is supported. It has built machine learning algorithms to test ideas and deploy models without writing long codes. It was originally designed to analyse data in the field of agriculture but it is now used by researchers, industrial scientists and educational institutions.

KNIME –

It is a free open-source platform for machine learning and data mining. It has an intuitive interface to create end to end data science workflows, starting from modelling to production. It has different pre-built components to enable fast modelling which is code less. It has a powerful set of instructions and integrations which makes it versatile and scalable to process complex data sets and use advanced sets of algorithms. Common use cases are credit scores, fraud detection and risk assessment of credits.

H20 –

It is open-source machine learning which aims to make AI accessible to everyone. It has support for common ML algorithms and has auto ML functions so users can build and deploy ML models in AI environments in a faster and simpler manner. It can be integrated with API and available in all major programming languages and uses distributed memory computing to analyse huge data sets in an efficient manner.

Orange –

Open-source data science toolbox to develop, test and visualize mining workflows. It is component-based software with a large collection of pre-built ML algorithms and text add-ons for mining. Molecular biologists and bioinformaticians can also use its extended capabilities. It allows interactive data visualization , numerous types of graphs such as Silhouette plots and sieve diagrams. Developers can mine data in Python and non-programmers can use drag and drop interface.

Apache Mahout –

It is an open-source platform over APACHE Hadoop. This framework focus is on three areas : engines recommended, clustering and classification. It is majorly suited for complex and large volumes of projects which require extensive data mining and used by leading companies like LinkedIn and Yahoo.

SAS Enterprise Mining –

It is an analytics and data management platform. It simplifies the data mining process and helps analytics professionals to uncover large volumes of data into meaningful information. It has interactive GUI ; users can generate data mining models and use them for critical business cases and it has a rich set of algorithms for preparation and exploration of data and build models which are advanced predictive and descriptive. It is used in fraud detection, resource planning, marketing campaigns and response rate enhancements.

Comparison Table: Data Mining Tools

Below table summarizes the differences between the top data mining tools:

TOOLS	TYPE	OPERATING SYSTEM	LANGUAGE	FEATURES
MonkeyLearn	ML , Data mining	Cloud platform	Python , Java, Ruby	Turn text into tags Text analysis models to auto text tagging
RapidMiner	Statistical, predictive data analysis, data mining	Cross platform	Language independent	20 new functions for data handling, new aggregation functions Intuitive GUI Macro viewer functionality
Oracle data mining	Advanced analytics , data mining	Cross platform	Java	Advanced analytics Advanced compression Supports multi-tenant architecture
IBM SPSS modeler	Data mining	Cross platform	GUI and Java	Support variety of scripting environments Mines data from databases and prepares for analysis
Weka	Machine learning	Cross platform	Java	49 data processing tools ,76 classification algorithms ,8 clustering, 15 attribute evaluators, 10 search algorithms
KNIME	Enterprise reporting, business intelligence, data mining	Linux, OS X , Windows	Java	Well defined APIs for plugin extension Data handling , intelligent automatic caching Import/export of workflows , parallel execution CLI version for ‘Headless’ , ‘batch execution’ and ‘Halting’
H20	Machine learning	Hybrid cloud	Java	Distributed, in memory processing Auto ML
Orange	ML, Data mining , visualization	Cross platform	Python C++, C	Visualization & programming Large toolbox, scripting interface Extendable documentation
Apache Mahout	Data mining	Linux	Java, Hadoop	Algorithms written on top of Hadoop Ready to use framework on large volume data mining
SAS Enterprise mining	Analytics and data management	Cross platform	GUI, SAS language	Ease of use with GUI Advanced predictive and descriptive modelling