Data mining refers to identifying patterns and sorting of large data sets to establish relationships in order to solve problems analytically. Being an interdisciplinary topic, data mining involves, machine learning, databases as well as algorithms. It is a significant part of modern business and information technology industry, where data is obtained from customers as well as operation and is mined to gain insight on business. Data mining tools are useful for enterprises to predict future trends and are useful in many research areas. These include mathematics, marketing, cybernetics and genetics. Data mining techniques provide method to drive efficiencies in addition to predicting customer behaviour. With an efficient aid of data mining techniques, a business can be set apart from the competition by using of predictive analysis techniques.

RapidMiner is a software platform meant for teams specialised in data science. It is an extremely powerful data mining tool used for the purpose of creating, delivering and maintaining predictive analysis. It is world’s leading open source stand-alone application used for data analysis and data mining experiments. RapidMiner is useful for both research as well as real world data mining tasks. It provides a complete workbench for business analytics as a GUI focusing majorly on text mining, data mining, machine learning and predictive analysis. It gives you an insight into making profitable decisions by using a wide variety of predictive and descriptive techniques.

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RapidMiner was developed in 2001 and is one of the world’s most-used solutions for data analysis today. RapidMiner and its extensions offer more than 1500 operations for various tasks in data transformation, analysis, and visualization. A few of the popular extensions include a connection functions to R, a machine learning library called Weka, text and web mining extensions as well as extensions for time series analyses.

The fundamental objective of RapidMiner has always been to find connections in extremely large data volumes. Additionally, RapidMiner provides the following major features:

· Stream mining: Only parts of the data are taken through the analysis process instead of holding complete data sets in the memory. The rest of the results are later on aggregated in a suitable location. Such part processes are carried out in distributed form e.g. in Rapid Analytics clusters or Hadoop.

·  In-database-mining: This extension supports taking the algorithms to the data instead of taking data to the algorithm. Thus, the execution of analysis is directly supported within databases. Initially, such a solution was only available from individual database providers like Oracle and IBM DB2 on a very limited basis. RapidMiner now offers this solution for numerous analysis procedures and database-wide.

· Radoop: Radoop is world’s first graphical connection of Hadoop to handle big data analytics, which means that even terabytes and petabytes of data can be transformed and analysed. Radoop therefore combines the strengths and features of RapidMiner with Hadoop. This results in a solution for graphical development and execution of workflows for predictive analytics on Hadoop clusters which includes support for the Hadoop file system, Hive and Mahout.

· Meta data propagation: Trial and error is no more required with the facility of inspection of the expected results as early as design time without having to wait for potentially lengthy process executions.

· Recommender: RapidMiner continuously analyses the analysis process created until now as well as gives new suggestions. In addition to helping the beginners, this accelerates the expert’s work tremendously.

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