Data Mining Tools and Techniques

Data is priceless and it is not very easy to analyze.

Data mining is the process in which the user searches and finds out different patterns among large sets of data so as to use it as a crucial information. With the help of this technique, the data experts use specific algorithms, statistical analysis, artificial intelligence and database systems to extract important information from large sets of data and use them for their own benefit. In simpler words, Data mining can also be defined as the process of searching and finding all the possible beneficial patterns or trends in data sets which are large in size. Data Mining is a technique that is used for finding out unfamiliar and new relationships amongst the various datasets.

There are many helpful and crucial tools available on the internet for Data mining. This article has collected a best few of Data Mining tools which are both open sources I.e., freely available and commercially available for use.

Data Mining Tools

Rapid Miner

Rapid Miner is a free to use tool. It was released in 2001 under the name of the developers- RapidMiner. It is mainly used for data preparation, machine learning, predictive analysis, etc. The programming language used is Java.

Rapid Miner Features:

  • It allows multiple data management methods.
  • Access to GUI (graphical user interface).
  • It is Integrated with native databases.
  • It gives us access to interactive and shareable dashboards.
  • Access to remote analysis processing.
  • Access to features like Data filtering, joining, merging, and aggregating.
  • It also builds, trains and validates predictive models
  • It gives us access to reports and notifications.

KNIME

KNIME is an open-source software. It is used for creating various data science applications. Knime is completely written in Java and it is based on Eclipse. It has GNU General Public License.

KNIME Features:

  • It gives us access to build a data science workflow.
  • It merges data from any source.
  • With the help of KNIME, the users can easily aggregate, sort, filter, and join data either on the local machine, in-database or in distributed big data environments.
  • It also helps in building machine learning models that are used for the purpose of classification, regression, dimension reduction.

SAS Data Mining

Statistical Analysis System is the full form for SAS. It was mainly developed for the purpose of analytics and data management. It gives the user complete access to a graphical user interface.

SAS Data Mining Features:

  • SAS Data mining tools can be used for the objective analyzing Big data
  • SAS Data Mining is an ideal tool for Data mining, text mining & optimization
  • SAS is also highly scalable. It offers allocated memory processing architecture

Oracle Business Intelligence

It was developed by Oracle Corporation and it was written in C++ and Java. Oracle Business Intelligence is an open-source and it can be used in machine learning and data visualization. The advantage of this tool is that it can be used by novice and expert.

Oracle Business Intelligence Features:

  • Interactive Data Visualization
  • It provides Interactive data exploration which can be used for rapid qualitative analysis
  • It also provides hands-on training and visual illustrations of various data science concepts
  • It gives users a large range of add-ons from external data sources in data mining

Python

Python is a high-level language which is open source and is very popular among new software developers. It was designed by Guido van Rossum under the parent company Python Software Foundation in 1991. The advantages of Python are that it is simple to learn and the syntax is easily understood.

Python Features:

  • It supports GUI (graphical user interface)
  • It is a high-level language, object oriented and interpreted language
  • It is completely open source and 100% free
  • It is a Portable language
  • It supports multiple inheritance

Weka

Weka is short for Waikato Environment for Knowledge Analysis. It was developed by University of Waikato. It has a license of GNU General Public License. It is written in Java. It is also a free software.

Weka Features:

  • It is platform independent
  • Open source and 100% free to use for the users
  • It is very simple to use and understand
  • It supports GUI (graphical user interface)
  • It has flexibility for scripting experiments
  • It has various data preprocessing tools

Teradata

Teradata was founded in late 1970s by Teradata Corporation. Teradata is useful as it can work on various server platforms such as Unix, Linux, and Windows. The basic motive is to provide database and analytics related software products and services to the users.

Teradata Features:

  • It supports SQL Database
  • It has low TCO ()
  • It has Linear Scalability which means it is highly scalable
  • It is massively based on Unlimited Parallelism
  • Teradata has a mature optimizer

Solver

Solver is an easy-to-use Data mining tool mainly meant for professional level. It is used for data visualization, forecasting, and Data mining in Excel.

Solver Features:

  • Solver provides users with a set of analysis features that are derived from both statistical and machine learning methods
  • The tool also allows to work with large sets of data
  • It has built-in features available for data exploration and visualization

Orange Data Mining

Orange is an open-source toolkit, which mainly used in data visualization, machine learning and data mining. It was developed by University of Ljubljana, 24 years ago on October 10, 1996. It is written in C++, C, Python and Cython.

Orange Data Mining Features:

  • It is completely open-source
  • It supports Hands on training and visual illustrations
  • It also has interactive data visualization
  • It supports visual programming
  • It also has extended functionality feature available

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