Data mining refers to the process of analyzing and exploring data sets in order to discover a meaningful pattern. Data mining tools are used in order to break down the data mining into six phases which includes business understanding, data understanding and preparation, modelling, evaluation and presentation. It symbolizes a sequence of events through the data mining process and serves as a guideline for a repetitive cycle rather than a linear process.
Data Mining Process
1. Understanding of the business
Firstly, users need to figure out the required situation and what they look to accomplish through data mining for your business. They need to define the problem, identify the business goals and then set up a plan to proceed.
2. Understanding of data
Users need to determine which type of data is required and then gather that data from all available sources.
3. Preparation of Data
It is a critical step in the data mining process where users can select, cleanse, construct and merge data thereby preparing it for analysis. While it may be time-consuming, data preparation can help you ensure the most accurate results by cleansing data and turning raw data into a solution which can actually work.
It is the core of any machine learning project and lets the user decide which modelling technique to use to answer the project goals. It consists of analyzing data, generating tables and using plots & graphs in order to reveal patterns.
5. Evaluation of results
Users can evaluate the model results in light of the originally defined goals to make sure the model is accurate, complete and highlights what insights are valuable. Depending on the insights uncovered by data mining, it may help identify objective and additional questions required to answer.
6. Presentation of data
The final step is to turn all of the work into something which is useful to others. The Users will take the results in order to determine a proper deployment strategy which ensures the analysis is comprehensible. It can be as simple as creating a conclusive report or as complex as maintaining a data mining process from the beginning to end. It also includes delivering presentations to the customer and summarizes the project findings.
Data mining and Business intelligence
Business intelligence and Data mining are connected as data mining find the ‘what’ in the data while Business intelligence discovers the ‘why’ and ‘how’. Data mining finds the required information while BI determines why it is important.
Data mining helps make sense of the block of big data and provides answers to questions you weren’t looking for. With machine learning, data mining can accelerate the repetitive task of data analytics and modelling. It can help uncover the unknown pattern and abnormalities in data sets.
Companies can use data mining tools in order to identify the patterns and connections which can help to understand the customer and their business thereby leading to increasing revenue and reducing risk.
With applications in an array of industries which include fraud detection, customer relationship management and more, it can also lead to improved sales forecasting and can influence customer satisfaction. Besides this data mining tools can identify relevant information in data sets and turn data into actionable sights for decision making.
List of Best Data Mining Tools and Software
Let us look at some of the top open-source tools which can help you get started with data mining.
KNIME stands for Konstanz Information Miner. It is an open source tool which can be used for integration, research, CRM, data analytics, data mining, enterprise reporting and business intelligence.
It is available in Mac, Windows and Linux operating systems and is considered to be a good alternative to SAS. Some of the prominent companies using Knime include Johnson & Johnson and Comcast.
- Available integrations
- Rich algorithm
- Organized workflows
- Easy to set up
- No stability issues
- Heavy on RAM
- Lacks adequate data handling capacity
- Can improve integration of graph databases
The platform is free to use.
CDH stands for Cloudera Distribution for Hadoop and it aims at enterprise class deployment. It is an open source and free platform which features Apache Spark, Apache Hadoop, Apache Impala and much more.
CDH allows you to collect, manage, process, model as well as distribute unlimited data.
- Administers Hadoop cluster well
- Easy to implement
- Offers high security
- Comprehensive distribution available
- Multiple installation approaches which may make it confusing
- Complicated UI features
- Expensive licensing price
CDH is a free data mining software by Cloudera while the cost of the Hadoop cluster per node is between $1000 to $2000 per terabyte.
It is free of cost and an open source platform. The DBMS is constructed in order to manage huge volumes of data spread across numerous commodity servers.
Cassandra is used by some high profile companies such as American Express, Accenture, General Electric, Facebook and Yahoo.
- Handle massive data
- Automated replication
- Simple ring architecture
- Log based structured storage
- Requires extra effort for troubleshooting
- Clustering can be improved
The tool is available for free.
It is an open source platform which facilitates data visualization in order to help users get precise, simple and embeddable charts quickly.
Prominent customers of Datawrapper include Fortune, Bloomberg, Twitter and The Times.
- It is device friendly
- Fast and Interactive
- Helps bring the chart at one place
- Customization and export options available
- No coding requirements
- Limited availability of color palettes
Free service is available. While for a single user and daily use it costs 29 €/month. In case of a professional team it costs 129€/month and the customized version is available at 279€/month.
5) Apache Hadoop
The Apache Hadoop is a software framework which can be employed to handle big data and clustered file systems. It helps process big datasets using the MapReduce programming model. Haddop is an open source framework written in Java and also provides cross-platform support.
Some of the big companies using the tool include Hortonworks, IBM, Intel, Facebook, Amazon Web Services and Microsoft.
- Ability to handle different data types such as video, images, JSON and XML
- Useful for R&D purpose
- Quick access to data
- Highly scalable
- Available resting service on a cluster of computers
- Disk space issues can lead to data redundancy.
- The I/O operations can be optimized to ensure better performance.
The software is free to use.
It is a free and open source tool for analytics, big data and visualization. The primary features include 2D and 3D graph visualizations, link analysis between graph entities, geospatial analysis, big data fusion and integration.
- Secure and Scalable
- Supported by a dedicated team
- Works well with Amazon AWS
The tool is free to use.
The full form for HPCC is High-Performance Computing Cluster. It is a complete big data solution in order to have a highly scalable supercomputing platform. It can also be referred to as a Data Analytics Supercomputer.
HPCC is written in C++ and has a data-centric programming language known as Enterprise Control Language. It is an open source tool and is a good substitute for Hadoop as well as other Big data platforms.
- Based on commodity computing clusters providing high performance
- Fast and highly scalable platform
- High performance online query applications
- Comprehensive and cost-effective
This tool is free to use.
Prominent customers of MongoDB include Google, MetLife, eBay and Facebook.
- It is easy to learn.
- The platform provides multiple technological support
- No problems while installing
- Low cost and reliable
- Limited analytics features
- Slow in case of certain cases
The platform’s SMB and the enterprise version are paid and the pricing is available on request.
9) Apache SAMOA
SAMOA stands for Scalable Advanced Massive Online Analysis and is an open-source platform in order to ensure big data stream mining. The platform allows you to create a distributed streaming machine learning algorithm and run it on multiple distributed stream processing engines.
- Easy to use interface
- Real-time streaming available
- Available with Write Once Run Anywhere (WORA) architecture.
The tool is free to use.
The platform features an open studio for big data which comes under free and open source license. The components of the platform include NoSQL and Hadoop. With a big data platform, it comes with a user data subscription license and provides web, email and phone support.
- Accomplish speed and scale
- Accelerate your real-time movement
- Manage multiple data sources
- Gather numerous connectors under a single roof
- Improved community support
- Difficult to add custom components
The open studio is free while the rest of the products are available on a subscription-based cost.
It is a cross-platform which offers distributed stream processing and provides a fault-tolerant real-time computational framework. The tool is written in Java and Clohure.
Storm is based on customized spouts and manipulations in order to allow distributed processing of data. Some of the prominent users of Storm include Yahoo, Groupon and the Weather Channel.
- Fast and reliable at scale.
- Guaranteed processing of data
- Offer real-time analytics
- Continuous computation
- Difficult to use
Use of Nimbus and Native Scheduler can become a bottleneck.
Talend is free.
Qubole is one of the best data mining tools and the data service is an inclusive Big data platform which manages, learns and optimizes itself based on your usage.
Popular companies which use Qubole include Adobe, Gannett and Warner Music.
- Increased flexibility
- Optimize spending
- Easy to use
- Eliminate any technology lockin
- Available across regions
The business edition is free to use and supports up to 5 users.
It is a cross-platform tool offering integrated data for machine learning, data science and predictive analytics. It comes with features for small, medium and large proprietary editions. Prominent Organizations which use Rapidminer include BMW, Samsung, Hitachi and Airbus.
- Runs to Open-source Java core.
- Has facility of code optional GUI
- Integrates with APIs
- Excellent customer service
- Data services can be improved.
The small enterprise edition of Rapidminer will cost you $2,500 User/Year while the medium enterprise edition will cost $5,000 User/Year.
It is an open-source platform and is one of the most comprehensive statistical analysis packages. It is written in R, C, and Fortran programming languages.
Its features include data analysis, calculator, data manipulation and graphical display.
- Brilliant graphics
- Vast ecosystem
- Lack of memory management and speed
The R studio is free, while the commercial version is available at $995 per user per year.The
RStudio server pro commercial license is priced at $9,995 per year per server.
It is one of the best data mining tools for business intelligence and analytics. It presents a variety of products to aid organizations with visualizing and understanding data.
The software comprises three main products i.e.Tableau Desktop, Tableau Server and Tableau Online.
It is capable of handling different data sizes and provides real-time customized dashboards. A few prominent companies which use Tableau include Grant Thorton, ZS associates and Verizon Communications.
- Flexibility to create different type of visualizations
- Data blending capabilities
- Bouquet of smart features
- Mobile ready and shareable dashboard
- Can improve the Formatting controls
The tool offers different editions for desktop, server and online and the pricing starts from $35/month.
There are an array of data mining tools available. Some of the popular ones include:
Tableau: Offers great visualization and data analytics features to solve your business problems.
Rapidminer: Offers machine learning procedure for data reprocessing, predictive analytics and statistical modelling.
So, there you have it folks, let us know in the comments which is the most suited data mining tool for you?