Today’s savvy companies know that process optimization means maximizing profit by using existing human and capital assets. Optimization is achieved by improving yields, quality, and throughput while minimizing capital expenses.
Oftentimes, companies spend a lot of time and money collecting data, but they don’t make full use of the data collected. With a data mining tool, valuable information can be extracted from large databases saving time and improving processes.
Top 10 Data Mining Tools To Supercharge Your Data
1.RapidMiner Studio
This is a visual design environment that can help you quickly develop a predictive analytic workflow. RapidMiner Studio comes with a library loaded with machine learning algorithms, model evaluation tools, data preparation and exploration functions.
Data analysts can use the existing R and python code to add new functionalities via a marketplace which has many pre-built extensions that will help your data mining processes to be more effective.
RapidMiner use cases:
- Machine learning
- Text mining
- Data mining
- Predictive analysis
- Business analysis
2.Knime Analytics Platform
Knime is a reporting and integration platform that combines various components for use in data mining and machine learning. It works by using a data pipeline concept that has a graphical interface that allows the use of nodes for data processing. Very few open source solutions have a wide range of features like Knime.
It comes as a suite of software applications that include collaborative extensions like Knime Server Lite, Knime TeamSpace, Knime Server and Knime WebPortal.
With this data mining tool, you’ll get access to more than 1000 data analytic routines either through R and Weka or natively.
Knime use cases:
- Image processing
- Text analytics
- Social media analysis
- Network analysis
- Data mining
