Top 14 Data Mining Tools
Introduction to Data Mining
Let us get into Top Data Mining Tools. Data mining is a specialist space in the field of business analytics. Data mining is the activity that a business engages in to find meaningful information from all the sources of data it can provision-loosely termed as raw data, employing intelligent and scientific techniques, also called algorithms.
Picturing data mining as a machine, the raw data becomes the input, the data mining activity becomes the task the machine is designed to do and the output from the machine is actionable data, in other words, data that can be used to take strategic or tactical decisions, positively impacting the bottom line. So, what does the machine itself in the below figure represent? The machine in this oversimplified model is the tool that is used to execute the various methods and techniques used in data mining.
Our discussion here will be around this machine we identified as the tool used to execute the data mining techniques. Data Mining tools are software programs that help in framing and executing data mining techniques to create data models and test them as well. It is usually a framework like R studio or Tableau with a suite of programs to help build and test a data model.
There are many tools in the market both open source and proprietary with varying levels of sophistication. At the root, each tool helps with implementing a data mining strategy, but the difference lies in the level of sophistication you the customer of these software needs. There are tools that do well in a specific domain such as the Financial domain or the Scientific domain.
Let’s look at the more popular ones in the market.
- Rapid Miner
- Oracle Data Mining
- IBM SPSS Modeler
- Knime
- Python
- Orange
- Kaggle
- Rattle
- Weka
- Teradata
- H2O
- Apache Spark
- Sisense
- Xplenty
1. Rapid Miner
A data science software platform providing an integrated environment for various stages of data modelling including data preparation, data cleansing, exploratory data analysis, visualization and more. The techniques that the software helps with are machine learning, deep learning, text mining and predictive analytics. Easy to use GUI tools that take you through the modelling process. This tool written entirely in Java is an open-source framework and is wildly popular in the data mining world.
2. Oracle Data Mining
Oracle, the world leader it database software, combines its prowess in database technologies with Analytical tools and brings you the Oracle Advanced Analytics Database part of the Oracle Enterprise Edition. It features several data mining algorithms for classification, regressing, prediction, anomaly detection and more. This is proprietary software and is supported by Oracle technical staff in helping your business build a robust data mining infrastructure at the enterprise scale.
The algorithms integrate directly with the Oracle database kernel and operate natively on data stored in its own database, eliminating the need for extraction of data into standalone analytics servers. The Oracle Data Miner provides GUI tools taking the user through the process of creating, testing and applying data models
3. IBM SPSS Modeler
IBM is again a big name in the data space when it comes to large enterprises. It combines well with leading technologies to implement a robust enterprise-wide solution. IBM SPSS Modeller is a visual data science and machine learning solution, helping in shortening the time to value by speeding up operational tasks for data scientists. IBM SPSS Modeler will have you covered from drag and drop data exploration to machine learning.
The software is used in leading enterprises for data preparation, discovery, predictive analytics, model management and deployment. The tool helps organizations to tap into their data assets and applications easily. One of the advantages of proprietary software is its ability to meet robust governance and security requirements of an organization at the enterprise level, and this reflects in every tool that IBM offers on the data mining front.
4. Knime
Konstanz Information Miner is an open-source data analysis platform, that helps you with build, deployment and scale in no time. The tool aims to help make predictive intelligence accessible to inexperienced users. It aims to make the process easy by it is a step-by-step guide-based GUI tool. The product markets itself as an End to End Data Science product, that helps create and produce data science using its single easy and intuitive environment.
5. Python
Python is a freely available and open-source language that is known to have a quick learning curve. Combined with its ability as a general-purpose language and it is large library of packages that help build a system for creating data models from the scratch, Python makes for a great tool for organizations who want the software they use to be custom-built to their specifications.
With Python, you won’t get the fancy stuff that proprietary software offers, but the functionality is there for anybody to pick up and creates their own environment with graphical interfaces of their liking. What also supports python is the large online community of package developers who ensure the packages on offer are robust and secure. One of the features Python is known for in this field is powerful on the fly visualization features it offers.
6. Orange
Orange is a machine learning and data science suite, using python scripting and visual programming featuring interactive data analysis and component-based assembly of data mining systems. Orange offers a broader range of features than most other Python-based data mining and machine learning tools. It is software that has over 15 years of active development and use. Orange also offers a visual programming platform with GUI for interactive data visualization.
7. Kaggle
The largest community of data scientists and machine learning professionals. Kaggle although started as a platform for machine learning competitions, is now extending its footprint into the public cloud-based data science platform arena. Kaggle now offers code and data that you need for your data science implementations. There are over 50k public datasets and 400k public notebooks that you can use to ramp up your data mining efforts. The huge online community that Kaggle enjoys is your safety net for implementation-specific challenges.
8. Rattle
The rattle is an R language-based GUI tool for data mining requirements. The tool is free and open-source and can be used to get statistical and visual summaries of data, the transformation of data for data models, build supervised and unsupervised machine learning models and compare model performance graphically.
9. Weka
Waikato Environment for Knowledge Analysis (Weka) is a suite of machine learning tools written in Java. A collection of visualization tools for predictive modelling in a GUI presentation, helping you build your data models and test them, observing the model performances graphically.
10. Teradata
A cloud data analytics platform marketing its no code required tools in a comprehensive package offering enterprise-scale solutions. With Vantage Analyst, you don’t need to be a programmer to code complex machine learning algorithms. A simple GUI-based system for quick enterprise-wide adoption.
11. H2O
H2O is an open-source ML platform that aims to make artificial intelligence (AI) technology available to everyone. It supports the most common ML algorithms to assist users in quickly and easily building and deploying ML models, even if they are not experts. H2O can be integrated via an API, which is available in all major programming languages, and it employs distributed in-memory computing, making it ideal for analyzing large datasets.
12. Apache Spark
A powerful analytics engine, Apache Spark comes with a slew of APIs that encourage Data Scientists to repeatedly access data for Machine Learning, SQL Storage, and other purposes. You can build parallel apps with Apache because it’s an all-powerful, iterative, open-sourced, and in-memory distributed analytics engine. Over 13,424 companies use it because of its features such as ease of use, scalability, speed, and high-performance analysis on huge datasets.
13. Sisense
This data mining software can be used to analyze large, diverse datasets and generate specific business patterns when it comes to creating reports for an organisation. It also includes a variety of widgets that make it easier to create graphs, pie charts, and other similar reports.
Data from multiple sources can be combined and stored in a single repository using Sisense. For a non-technical audience, you can also create reports with rich visuals based on the data you’ve refined.
14. Xplenty
Xplenty offers a platform with data integration, processing, and preparation capabilities for analytics. With the help of Xplenty, businesses can take full advantage of the possibilities presented by big data, all without having to spend any money on personnel, hardware, or software. An all-in-one solution for creating data pipelines.
With a rich expression language, you can perform complex data preparation tasks. It has an easy-to-use ETL, ELT, or replication solution implementation interface. A workflow engine will let you orchestrate and schedule pipelines.
Conclusion
So there you have it, an impressive list of comprehensive tools and frameworks that help you build a data ecosystem for building, testing and implementing data models that enable you to derive value out of your data at an enterprise scale.
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