The total amount of data generated worldwide increases by 40% every year. Many companies seek to make use of the constantly increasing mountain of data in order to increase their e-commerce business. But making use of such Big Data alone doesn’t add any value — enter data mining. Below you can find a discussion of the various analysis approaches involved in data mining, to give you an idea of how...Data mining: analysis methods for big data
More and more companies have large amounts of data that are valuable resources for customer segmentation, sales management, and target marketing. However, if these data sets cannot be sufficiently analyzed and evaluated, they are practically worthless to companies. There is a wealth of information here, but only those who know how to use it can benefit from it. This is also pointed out by trend researcher and futurologist John Naisbitt with his well-known quote:
“We are drowning in information, but starving for knowledge.”
– Trend researcher and futurologist, John Naisbitt, on growing volumes of digital data
Data mining tools help to manage the amount of data and identify potentially decisive trends and patterns. Data mining software is becoming increasingly complex and the selection of tools is growing. To help you keep track of the most important data mining programs, we have compiled a comparison of the various data mining programs available.
Techniques, tasks, and components of data mining
Data mining is the term used for algorithmic methods of data evaluation that are applied to particularly large and complex data sets. Data mining is designed to extract hidden information from large volumes of data (especially mass data, which is known as Big Data), and therefore identify even better hidden correlations, trends, and patterns that are depicted in them. This is where data mining tools come in.
The term 'data mining' does not mean generating data or even the data set themselves, but refers to the practice of data analysis. Many of the methods used come from statistics; however, data mining is not purely statistical, but rather an interdisciplinary method that connects computer science and mathematical findings with machine-learning technologies (especially unsupervised learning) and artificial intelligence. These powerful methods are integrated into data mining software to enable large data sets to be evaluated.
Text mining is a special form of data mining, which gains special relevance due to the popularity of language software and language technology. Information retrieval here does not refer to data sets, but to text documents. The main points are extracted from large amounts of text (specialist articles or company documents). This makes text mining useful for companies when researching new projects, for example.
Nevertheless, users must also have a good understanding of data sets in order for data mining to be successful. Only then can they use the data mining tools in a meaningful way – programming skills are not required.
Individual data mining tasks:
- Classification: Assigns individual data objects to certain predefined classes (such as cats or bicycles) that were not previously assigned to these classes; the decision tree analysis is particularly helpful for classification.
- Deviation outlier analysis: Identifies objects that do not comply with the rules of dependency for related objects; this enables you to find the causes of the discrepancies.
- Cluster analysis: Identifies clusters of similarities and then forms groups of objects that are more similar in terms of certain aspects than other groups; unlike classification, the groups (or clusters) are not predefined and can take different forms depending on the data analyzed.
- Association analysis: Reveals correlation between two or more independent items that are not directly related, but occur more often together.
- Regression analysis: Reveals relationships between a dependent variable (e.g. product sales) and one or more independent variables (e.g. product price or customer income), and is used, among other things, to make forecasts about the dependent variable (e.g. a sales forecast).
- Predictive analytics: This is actually a superordinate task that aims to make predictions about future trends. It uses data mining, among other things, and works with a variable (predictor) that is measured for individual people or larger entities.
With the help of association analysis, informative correlations could be established during purchasing decisions for different products, which significantly improved the shopping basket analysis. This method is used to determine recommended purchases from online mail order companies.
The different methods can be roughly divided into so-called observation problems (deviation analysis, cluster analysis) and forecasting problems (regression analysis, classification). A detailed explanation of different data mining methods can be found on Zentut.
A comparison of data mining tools
In order to carry out a comparison of the best data mining tools, we will introduce the tools, RapidMiner, WEKA, Orange, KNIME, and SAS. It has been proven that users use multiple programs, because data mining tools have different strengths that can be combined with each other. Data mining tools are often compatible with each other. But even with just one good all-rounder tool, you can do a lot of things as a beginner.
RapidMiner (formerly known as: YALE, 'Yet Another Learning Environment') is one of the most popular data mining tools. In 2014, it was the most widely used data mining tool prior to the R tool, according to a survey conducted by KDnuggets. It is available for free and easy to use even if you don’t possess special programming skills. Nevertheless, it offers a large selection of operators. Startups, in particular, make the most of this tool.
RapidMiner was written in Java and contains more than 500 operators with different approaches to point out connections in data – there are options for data mining, text mining, web mining, and also for mood analysis (sentiment analysis, opinion mining), among other things. The program also imports Excel tables, SPSS files, and data sets from many databases, and integrates the WEKA and R data mining tools. This makes it a comprehensive all-rounder.
RapidMiner supports all steps of the data mining process, including the presentation of results. The tool consists of three major modules: RapidMiner Studio, RapidMiner Server, and RapidMiner Radoop, each of which executes different data mining techniques. In addition, RapidMiner prepares the data prior to analysis and optimizes it for faster subsequent processing. For each of these three modules, there’s a free and a fee-based version available.
A particular strength of RapidMiner is predictive analytics, which is the name given to predicting future developments based on collected data. When comparing data mining software, RapidMiner is one of the strongest tools out of the ones mentioned.
WEKA (Waikato Environment for Knowledge Analysis) is open source software and was developed by the University of Waikato. The data mining tool is based on Java and can be used with Windows, MacOS, and Linux. Known for its extensive machine learning capabilities, it supports all major data mining tasks such as clustering, association, regression, and classification.
The graphic user interface facilitates access to the software. In addition, WEKA offers connect to SQL databases and can further process the requested data. WEKA’s strength lies in classification: the data mining tool is known for its many classifications, including artificial neural networks, decision trees, ID3, and C4.5 algorithms. However, WEKA is less powerful when it comes to other techniques such as cluster analysis. Only the most important procedures are offered by this program.
Another disadvantage: WEKA can experience problems with processing if the amount of data becomes too much. This is because the data mining tool tries to load all of it into the memory. To avoid this, WEKA offers a simple command line (CLI) that makes it easier to handle large amounts of data.
WEKA was awarded the 'SIGKDD Service Award' from the Association for Computing Machinery for its high-research contribution. In comparison to other data mining tools, WEKA has proven particularly useful for teaching and research purposes.
The data mining tool Orange has existed for more than 20 years and is a project from the University of Ljubljana. The software’s core was written in C++, but early on the program was extended by the programming language, Python, which is now used as the query language. The more complicated operations are still carried out in C++. Orange is a comprehensive data mining software that demonstrates how much you can do with Python: It offers useful applications for data and text analysis as well as features for machine learning. When it comes to data mining, it works with operators for classification, regression, clustering, and much more. This data mining tool also integrates visual programming.
What is striking about the tool is that users repeatedly emphasize how fun this data mining software is compared to others. Both beginners and experienced users have admitted to being fascinated by Orange. Its popularity comes down to two things: firstly, the appealing data visualization that makes it more interesting to work with; secondly, the speed and ease with which the visualization takes place. The program prepares input data visually and instantly. Understanding these graphics and processing the data analysis further is relatively easy, and quick business decisions can be made. This makes Orange an ideal tool for data mining.
A further advantage for beginners is that there are numerous online tutorials available for the tool. Another special feature of Orange is that it learns the preferences of its users over time and reacts accordingly. This is another plus for the data mining tool.
KNIME was developed by the University of Constance and is now popular with a large international community of developers. Although KNIME was originally intended for commercial use, it is still available as open source software. It was written in Java and edited with Eclipse. If you compare this data mining software with others, its range of functions is especially impressive: with more than 1,000 modules and ready-made application packages, this tool helps to reveal hidden data structures. The modules can be expanded by additional commercial features.
Among its functions, integrative data analysis is particularly appealing – KNIME is one of the most powerful tools in its field and enables numerous methods of machine learning and data mining to be integrated. It is also particularly effective when preprocessing data i.e. extracting, transforming, and loading data. Its modular pipelining makes it a data flow-oriented data mining tool.
KNIME has been used in pharmaceutical research since 2006 and is also a powerful data mining tool for the financial data sector. However, KNIME is also frequently used in the business intelligence (BI) sector. Here, KNIME is regarded as the tool that made predictive analytics also available to inexperienced users. The tool is also interesting for beginners, because despite its many strong features, you don’t need much time to familiarize yourself with it. KNIME is available as a free program as well as a paid program.
SAS (Statistical Analysis System) is a product of the SAS Institute, one of the world’s largest privately-owned software companies. SAS is the leading data mining tool for business analysis and is also the most expensive of the programs listed here. However, it is the one that is best suited for use in large companies.
SAS is particularly good when it comes to the prognostic sector and interactive data visualization, which is ideal for large presentations. In principle, this data mining software provides a comprehensive all-round solution for successful data mining. The tool is characterized by very high scalability, so it’s possible to increase the performance proportionally by adding additional hardware or other resources. This also makes it a powerful tool for high-quality business solutions. For technically less experienced users, it has a graphical user interface.
However, this software can only be used free of charge if you get a corresponding license from a public institution. SAS is usually subject to a fee. The costs are decided upon request and depend on special conditions i.e. it’s cheaper for authorities or educational institutions. SAS is one of the more expensive alternatives among commercial tools. However, it is possible to customize the range of functions and therefore influence the price.
SAS is mainly used in pharmaceutical companies where it has established itself as standard. It is also frequently used in the banking sector and offers optimal solutions for BI and web mining. Among other things, it has its own business intelligence software for this purpose. This makes it one of the most powerful data mining tools on the market.
Data mining tools at a glance
After providing a detailed comparison of the data mining software, here’s an overview of all important features of the data mining tool:
Strong all-rounder with a special strength in predictive analytics
Various fee-based versions
Many methods of classification
Free software (GPL)
Creates particularly appealing and interesting data visualizations without the need for extensive prior knowledge
Software core: C++
Extensions and query language: Python
Free software (GPL)
The leading open data mining tool that has made predictive analytics available to the general public
Free software (GPL)
(from version 2.1 onwards)
Expensive, but powerful data mining software for large enterprises
Limited freeware available through educational institutions
Price only available on request
Various extensive models available