In a world where billions of bytes of data are generated every second, it is critical to find ways to separate meaningful data from raw and unprocessed data in order to extract useful insights and make business decisions. This is where Data Mining enters the picture. It is also critical to make an informed decision about which data mining tool to use. In this article, we will look at what Data Mining and Data Mining Tools are, as well as dive deep into the features of some of the most commonly used Data Mining Tools today.

Top Data Mining Tools

Now that we understand what Data Mining tools are and what they are used for, let us look at some of the most promising Data Mining tools available today and the features they provide:

Rapid Miner

RapidMiner is a data science software platform designed to provide a unified environment for data preparation, machine learning, deep learning, text mining, and predictive analytics, among other things. RapidMiner can be used to create business and commercial applications. It can also be used for machine learning research, training, rapid prototyping, and application development. It also supports all stages of the machine learning process, such as data preparation, visualisation of results, model validation, and optimization.

RapidMiner is built on an open core architecture (Open core model is a business model which is used for the monetization of commercially produced open-source software). The RapidMiner programme was entirely written in the Java programming language. It allows us to experiment with a large number of arbitrarily nestable operators that are detailed in XML files and created using the rapid miner's graphical user interface. 

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MonkeyLearn

MonkeyLearn is one of the most user-friendly machine learning platforms for text mining (the process of deriving high-quality information from text). It can be used to perform real-time data mining efficiently. The MonkeyLearn Data Mining tool is capable of performing a variety of data mining tasks such as detecting topics, analysing human sentiments (activity recognition) and intent, extracting keywords, and named entities. It can also be used to automate ticket tagging and routing in customer support and detect negative feedback in social media.

MonkeyLearn can also be used to deliver fine-grained insights from data mining activities, which could lead to better decision making. Our analysed data can also be linked to MonkeyLearn Studio. It is nothing more than a customizable data visualisation dashboard that makes it easier to discover trends and patterns in our data.

Oracle Data Mining

Oracle Data Mining is an Oracle Advanced Analytics Database representative. A number of large corporations around the world are using it to maximise the potential of their data to make precise predictions.

Oracle Data Mining employs a powerful data algorithm to target the most suitable customers. Furthermore, it identifies anomalies as well as cross-selling opportunities, allowing users to apply a different predictive model based on their needs. It also customises customer profiles as desired. Because of this personalised user experience, more and more customers are drawn to their applications, resulting in increased revenue.

Python

Python is a free and open-source programming language. It is extremely easy to use, and unlike R, the learning curve of Python is so short that it quickly becomes second nature. In Python, it is very simple to create datasets and perform extremely complex affinity analysis in minutes.

Data visualisation is the most popular business use case for Python. If you are familiar with basic programming concepts such as variables, data types, functions, conditionals, and loops, data visualisation in Python is extremely simple and straightforward. Python also includes a number of libraries, such as NumPy and Pandas, that aid in the application of Machine Learning Algorithms and statistical analysis for Data Mining.

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Kaggle

Kaggle is the world's largest community of data scientists and machine learning experts. Kaggle began as a platform for machine learning competitions. It is, however, expanding its domain into public cloud-based data science platform areas. It provides code and data that we require for our data science implementations. Kaggle has over fifty thousand public datasets and four hundred thousand public notebooks that we can use to help you with your data mining efforts. Kaggle's massive online community serves as a safety net for implementation-specific challenges.

Conclusion

In this article we have discussed about the some important data mining tools and we hope that it is helpful to you.