RapidMiner and Weka Data Mining Tools

The computerization of the current society has significantly improved techniques and public expertise in both the collection and generation of data from different sources. Almost every aspect of the human life is associated with a substantial amount of information. The upsurge in data for transit and storage has created an urgent necessity for the new technological development regarding the introduction of automated tools. As a result, it is supposed that the machine intelligence will help the humankind in transmogrifying information in order to generate relevant knowledge that can transform the lives of people. The need for such a transformation has given rise to an auspicious cutting edge that is known as data mining in the field of Computer Science, and several applications of it. This information technology technique is also called the knowledge discovery from data. Data mining refers to the process of extracting patterns that represent information, which is kept in data warehouses, databases, the web, and other repositories. This paper focuses on two data mining tools that currently exist in the market; it compares RapidMiner and Weka.

A fundamental step in data mining targets the discovery of information from the existing data repositories in the form of appropriate patterns and algorithms. The process assists in designing and working with a vast amount of data sets that are obtained from various sources. The primary feature of the technique is that it deals with complex data, the volume of which is organized in the range of gigabytes and terabytes. The mathematical analysis and multiplex algorithm are applied in order to obtain data tendencies or trends. Data mining becomes increasingly relevant to the prevailing circumstances in the business, industries, and institutions, especially in decision-making. Since the technique enables the identification of facts, any inconsistencies, trends, and sequences in a large database, it is most applicable in research, healthcare, and financial institutions.

Similarities and Differences between RapidMiner and Weka

Initially, RapidMiner was called Rapid-I. Currently, the RapidMiner Company is in the process of developing a tool for advancing its function and efficiency in Germany. Unlike RapidMiner, the Weka tool was developed at the University of Waikato in New Zealand. Both RapidMiner and Weka are Java-based software. RapidMiner was released to the market in 2006, while Weka was introduced in 1993. As such, RapidMiner is more recent than Weka. Both tools are open source; hence, they are available to users for free and can undergo modification. However, RapidMiner has only the previous version available for free. In such a manner, version 5 and the earlier ones are available to users without any charges. Nevertheless, the sixth version has various license options that include Enterprise, Professional, Personal, and Starter version. The Starter version is free but has some restrictions regarding the memory size allocation. The maximum memory provided in the free version is 1 GB, and the input files include Excel and comma-separated value one. In turn, Weka is totally free for non-commercial purposes.

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Unlike Weka, RapidMiner provides an integrating environment with a GUI (Graphic User Interface) that is customized in order to be user-friendly. RapidMiner focuses on subprocesses, which contain visually presented operators (Miner et al., 2012). Operators are applications of data sources, database management procedures, and data sinks. The construction of the data flow is performed via drag-and-drop actions and connection of outputs and inputs of corresponding operators. In the case of Weka, it provides four options for the database management. They include the Knowledge flow, Experimenter, Explorer, and command-line interface (Correia, Adeli, Reis, & Teixeira, 2016). They serve as the graphical user interfaces. Most people like the Explorer option. Explorer enables the data source definition, machine learning algorithm, visualization, and data preparation. The Experimenter option executes a similar dataset performance comparison for various algorithms. A simple command-line is an interface that allows users to type different commands.

RapidMiner provides an application wizard that automatically builds processes based on the intended objective, for example, sentiment analysis, direct marketing, or churn analysis. The tool has inbuilt tutorials for various tasks. The availability of the learning curve in the tool makes it more powerful besides the operators. Extensions that make RapidMiner popular include the time series analysis, web mining, and text mining. The number of executed databases management approaches increased due to the presence of Weka operators through extension. RapidMiner enables its users to import data from several databases for the further examination and analysis within its system since it supports different types of data. As for Weka, the knowledge flow is similar to the operator model of the RapidMiner tool. It is true because Weka permits a user to provide specifications regarding the data flow using requisite visible parts that are connected to the system. The main difference between the two systems in terms of the data flow is the visual presentation.

Unlike Weka, which is the most appropriate software for extracting association rules and providing machine learning methods, as well as specializes in regression and classification issues, RapidMiner is designed to offer solutions in various fields including medical diagnosis, detection of credit card fraudulence, text mining, targeted marketing, and financial and weather forecasting. However, the tool mostly focuses on the statistical computing and predictive analysis. The software is installable in any operating system (Chisholm, 2013). It contains over one hundred tutorial schemes for the clustering analysis and regression organization. It supports different file formats similar to Weka that supports several file formats including binary, C4.5, Comma-Separated Files, and ARFF (Lu, 2013). At the same time, RapidMiner has more appealing visual components than Weka does.

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Unlike RapidMiner that has a capability of the high connectivity with other application software, Weka tends to suffer from what people call a Kitchen Sink Syndrome, which refers to the constant update of the tool’s systems. The software does not have a good connectivity with other applications, for example, non-Java based data banks and Excel spreadsheet. The system’s reader of the comma-separated files is not as flexible as that of the RapidMiner. Weka lacks the fine polish that can be witnessed in the RapidMiner data mining tool. It is also weak; hence, it does not provide functions on the classical statistics. The software is not built with requisite facilities for enabling it to save scaling parameters that could apply to future datasets. Weka is not fitted with components that allow for the optimization of parameters for statistical approaches and machine learning.

Similar to RapidMiner, Weka can support numerous model assessment processes. However, it lacks several visual and data survey approaches that are available in RapidMiner. It also performs fewer clusters and descriptive statistics. Efforts have been made by the later with the view to enhancing its functionality in clustering. Deep learning techniques have not been put into consideration in this tool. Similarly, the massive data support, semi-controlled learning, and text mining are limited in Weka as compared to RapidMiner.

Both Weka and Rapid Miner use a cross platform in their portability. Weka is compatible with HSQLDB, SQLite 3.x, Oracle, ODBC, MSQL Server, MySQL, and PostgreSQL. RapidMiner is compatible with Mysql, Microsoft SQL Server, Oracle, DB2, IBM, Excel, SPSS, and Access. As such, both tools have compatibility with Oracle, MySQL, and MSQL but also differ in several other compatibility modes. Concerning flexibility, both Weka and RapidMiner are easy to use. However, Weka lacks the flexibility that is experienced in RapidMiner. Weka maintains its GNU General Public license, while RapidMiner uses the AGPL proprietary (Chauhan & Gautam, 2015). There are a total of 49 data processing facilities in Weka, eight clustering algorithms, 79 regressions or classification, three association finder rules algorithms, ten search feature selection algorithms, and 15 subset evaluator or attributes. As for RapidMiner, there are 500 operators for learning processes for the machine. RapidMiner includes attributes evaluators and learning schemes that are present in Weka (Chauhan & Gautam, 2015). As RapidMiner gives its users an option of the drag-and-drop interface when they are designing analytics; Weka provides the explorer and command line as the main interaction platform.

Table 1 Comparison


Date of Release

latest version

Latest version date of release

Operating System







Cross Platform

GNU General




version 6


Cross Platform

AGPL Proprietary

Language Independent

Source: (Rangra & Bansal, 2014).


The modern IT market offers several data mining tools that provide a solution to different problems that human beings face in decision-making. Two of the commonly used software are RapidMiner and Weka. Both tools have their strengths and weaknesses. However, RapidMiner is more advanced as compared to Weka. RapidMiner is enhanced with the model assessment tools that use independent validation and cross-validation sets. It also has numerous data transformation, data integration approaches, and data model and analysis (Rangra & Bansal, 2014). It also contains much visualization than any other solution in the market does. It is more suitable for individuals that are used to dealing with database files such as the business environment and academic setting. In turn, the Weka software is an open source tool that was created at the University of Waikato; today, it is offered for free for the non-commercial purpose. Currently, the tools popularity is growing among users due to its increased number of database management procedures. The system can assist in the development of new learning schemes. Weka supports several file formats including binary, C4.5, Comma-Separated Files, and ARFF. However, its popularity has not surpassed that of RapidMiner in the academic and business spheres due to its high demand for resources in executing DM algorithms. Weka is a robust tool with great versatility; as a result, it enjoys a significant support of the community.

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