Powerful Data Mining Tools for Your Data Mining Projects

Data is priceless and using that data for business purposes or projects is not as easy as it sounds. Data mining projects involve the usage of tools at different stages. The article covers some of the most popular and powerful data mining tools offering a myriad of utilities that facilitate the development of a data mining project.

To learn more about data mining, read – What is Data Mining

Popular Data Mining Tools

Below are some of the most commonly used data mining tools –

RapidMiner

Rapidminer is the world leader in open-source data mining due to the combination of its premium technology and its range of functionalities. RapidMiner application covers a wide range of data mining. In addition to being a flexible tool for learning and exploring data mining, the graphical user interface aims to simplify use for complex tasks in this area.

RapidMiner Features

• Prototype system for knowledge discovery and Data Mining
• An Open-Source type software licensed under the GNU GPL, based on java
• Works under Windows and Linux platforms
• Possesses over 400 operators that can be combined
• Uses the XML scripting language to describe operators and their configuration
• Ability to hierarchize operator strings and build complex operator trees
• The encryption language automatically allows a large number of experiments
• Has a graphical interface, command line, and Java API to use RapidMiner from your own programs
• A large number of extensions (plugins)

RapidMiner Applications

Text Mining, Multimedia Mining, among others.

Also Read – Data Mining Functionalities – An Overview

Microsoft SQL Server 2005

Microsoft SQL Server 2005 offers an integrated environment and works well with data mining models. The SQL Server Data Mining solution enables access to the information necessary to make intelligent decisions on complex business problems.

Microsoft SQL Server 2005 Features

• Database mirroring
• Offers over 12 result viewers for algorithms that allow a better understanding of the patterns found in the mining process
• Creates elevation graphs, profit graphs, and a classification matrix that allow comparing the quality of the models
• Allows creation of mining queries (DMX) similar to SQL that facilitates the task of creating data mining applications
• Has a graphical interface to generate DMX queries
• Possesses the most advanced machine learning algorithms: Naive Bayes, Clustering, Sequence Clusters, Decision Trees, Neural Networks, Time Series, Association Rules, Logistic Regression, and Linear Regression and text mining.

You May Like – Key Data Mining Applications, Concepts, and Components

Clementine

Clementine is a toolkit that offers an intuitive way to develop data mining applications. It allows developing predictive models and deploying them to improve decision-making. Clementine is designed to keep the requirements of business users, so you do not have to be a data mining expert to use it.

Clementine is one of the most advanced Data Mining tools, which combined modern modeling techniques with powerful data access, manipulation, and exploration tools in a simple and intuitive interface.

Clementine Features

• Easy understanding of the data
• Interactive data visualization
• Smart data preparation
• Combine data from multiple sources
• Specifies missing values
• Derive new variables
• Produces summary information
• Increase productivity with its visual approach to data manipulation

Must Explore – Data Mining Courses

Clementine Applications

Supervised Techniques – C&RT, Neural Networks, C5.0, Quest, CHAID, Linear Regression, and Logistic Regression

Unsupervised Techniques – K-means, Kohonen, Bi-stage, Apriori, GRI, Sequence, Carma, Anomaly Detection

Evaluation Techniques – Statistical Tables, Profit Graphs and ROI

Model Publication Techniques – Data Bases Scoring or Scoring, Scoring in real-time

Read our blog – What is data science?

KNIME

KNIME is developed on the Eclipse platform. It is programmed in Java. KNIME is conceived as a graphical tool with a series of nodes (which encapsulate algorithms) and arrows (which represent data flows) that are displayed and combined in a graphical and interactive way.

KNIME is an open-source tool that can be downloaded and used for free under the terms of the GPLv3 license with one exception that allows other users to use the well-defined API node to add proprietary extensions.

KNIME Features

• Manipulation of rows, columns, such as samples, transformations, groupings
• Visualization (histograms)
• Creation of statistical and data mining models, such as decision trees, regressions
• Model validation, such as ROC curves
• Scoring or application of said models on new data sets
• Creation of customized reports thanks to its integration with BIRT
• The graphical user interface allows easy and fast assembly of nodes for data processing (ETL: extract, transform, load), for data analysis, modeling, and visualization

Read More – Statistical Methods Every Data Scientist Should Know

KNIME Applications

KNIME integrates machine learning and data mining by splitting modular data pipelining. KNIME is extensively used in pharmaceutical research, CRM analytics, business intelligence, and financial data analysis.

Weka

Weka is a set of Java libraries used in knowledge extraction from databases. One of the most interesting properties of this software is its ease of adding extensions, modifying methods, among others. The Weka (Waikato Environment for Knowledge Analysis) package comprises a set of visualization tools and algorithms for data analysis and predictive modeling.

Weka is coupled with a graphical user interface for easy access to their functionalities.

Weka Features

• Freely available under the GNU General Public License
• Very portable because it is fully implemented in Java and can run on almost any platform
• Contains an extensive collection of techniques for data pre-processing and modeling
• Its graphical user interface allows easy to use for a beginner

Weka Applications

Weka supports various standard data mining tasks like data pre-processing, clustering, classification, regression, visualization, and selection.

Other Data Mining Tools

Some other helpful data mining tools are listed below –

SAS Enterprise Miner

SAS Enterprise Miner provides a large number of models and alternatives for data mining. It allows determining patterns and trends, explains known results, and identifies factors that ensure desired effects. In addition, it compares the results of the different modeling techniques, both in statistical and business terms, within a simple and easy-to-interpret framework.

SAS Analytics

SAS Analytics is a suite of analytical solutions that allow you to transform all the organization’s data into knowledge, reducing uncertainty, making reliable predictions, and optimizing performance.

KXEN

KXEN consists of a set of modules that can be purchased together, grouped in different packages, or separately. These modules can be used together with the “Modeling Assistant” and the “Robust Reporting” as an Automation Solution in Data Mining. They can also be integrated, via APIs, in a simple and transparent way.

Conclusion

There is no universal tool to successfully tackle any data mining project. Many of these tools can be used in the project, basis their features and usage. Historically, data mining tools predict future trends and behaviors, enabling business decision-making. The tools offer an almost tailored solution for a large number of projects that have these characteristics or simply that they are in charge of making decisions.

One of the most remarkable qualities in the chosen tools is their simplicity, both in their learning and in their application, thus reducing the costs of implementation. The choice of the data mining tools depends on the company’s needs, as well as the short and long-term objectives that it intends to achieve.

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