In today’s world, data is the cornerstone of smart decisions and companies need to use appropriate data mining tools to immediately get insights from their data. Data mining has become an integral part of analytics as it helps businesses for predictive modeling and maximize on the analytics programs. Businesses can use analytics models that are provided by data mining tools to get insights from large volumes of customer data and predict the behaviors of customers. Also, companies can use data mining tools and their analytics know-how to solve complex problems, price products and services more effectively, discover competitive strategies, predict performance, develop intervention strategies, identify market risk, predict potential problems, create more targeted marketing campaigns and improve customer relationships.
What Is Data Mining Software?
Data mining softwares are the software that allows you to extract usable data from a huge raw data to find patterns, anomalies and correlations. It’s results help companies to predict the result. Some key techniques like statistical analysis, algorithms, machine learning, database statistics and artificial intelligence are used by data mining software. Simply, data mining applications helps you get insights from huge volumes of data and transform it to actionable insights.
Top 11 Data Mining Softwares For 2021-
1. Sisense-
Sisense is one of the best data mining software which provides specific features to divide into massive datasets and discover crucial insights like customer’s shopping habits, search rankings and also other business analytics. It offers a compelling dashboard that makes it easy to explore and visualize large amounts of unprocessed data. Also it can be the best choice for beginners.
Features-
- It has a drag and drop feature that empowers data scientists in managing their projects with better productivity.
- You can connect with any number of data sources – both structured and unstructured.
- Sisense can be employed in supply chains, healthcare management, government institutions, enterprises, manufacturing and so on.
- The user interface is very attractive and dashboard provides highly workflow for visualizing large-scale data sources.
2. SAS Data Mining-
Statistical Analysis System (SAS) is a product of SAS Institute developed for analytics & data management. It can mine data, modify it and manage data from different sources and do statistical analysis. For non-technical users, it provides a graphical UI.
Features-
- This data miner allows users to analyze big data and derives accurate insight to make timely decisions.
- It has a highly scalable distributed memory processing architecture and is well suited for data mining, text mining and optimization.
3. Rapid Miner-
It is a free to use Data mining tool used for machine learning, model deployment and data preparation also. It’s wide range of products can be used to build new data mining processes and predictive setup analysis. Rapid miner is developed on the top of Java programming language and does precisely data mining.
Features-
- It has an attractive GUI interface with an additional command line version.
- It is a robust and flexible visual environment for predictive analytics that users can use to analyze big data without explicit programming.
- You can easily integrate rapid miner for linux in personalized data mining projects.
- I allows data filtering, joining, merging and aggregating
4. R-
It is a complete environment for statistical analysis of data and graphics. R is a highly flexible data mining platform which offers powerful analytical techniques like statistical tests, modeling, time series analysis, classification, clustering etc. It will be the best choice for those who have superior programming skills.
Features-
- It offers an effective and robust solution to store and handle huge amounts of corporate data.
- R is used for large-scale data mining projects and features an enormous list of pre-built solutions.
- Because of R’s robust error displaying abilities, it is easy to debug problems inside existing data mining projects.
5. Apache Mahout-
It is developed by Apache Foundation which serves the main purpose of creating machine learning algorithms. Mainly it focuses on collaborative filtering, data clustering and classification. Apache Mahout is written in JAVA and also includes JAVA libraries for mathematical operations like linear algebra and statistics. It is continuously growing because the algorithms implemented inside Apache Mahout are also growing. Many experts use apache mahout for real-time data mining including AOL, Apache, Drupal and Twitter.
Features-
- By integrating with Apache hadoop, it offers an excellent platform for those who are looking for distributed data mining solutions.
- It comes with native support for CPU/GPU/CUDA acceleration and allows you to leverage the maximum processing power you could get.
- Data scientists can use Mahout on top of Apache Spark as a backend to implement flexible and highly scalable data mining projects.
6. Rattle-
Rattle is a GUI based data mining tool which uses R stats programming language. Though it has an extensive and well-developed UI, it has an inbuilt log code tab which generates duplicate code for any activity occurring at GUI. Data sets that are generated by Rattle can be viewed and edited also. Rattle allows users to import datasets from either CSV or via ODBC and explore them for modeling their data mining solutions.
Features-
- It allows data scientists to develop and analyze complex data models and export them either as PMML or as scores.
- Data can be loaded from a variety of sources like TXT, Excel, CSV, ARFF, ODBC and RData Files.
- This data mining software can be used for large-scale data mining by corporations, governments and research institutions.
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