The Most Popular Tools Used in Data Mining

There is no looking further for the list of the most popular tools used in data mining as this article has comprehensively gathered and highlighted them down here for you to pick and make use of. Data mining, according research study, is the process of extracting and discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.

The goal is the extraction of patterns and knowledge from large amounts of data, not the extraction (mining) of data itself. It involves analysis of the extractable or extracted data. Aside from the raw analysis step, it also involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating.

But most particularly, the actual data mining task is the semi-automatic or automatic analysis of large quantities of data to extract previously unknown, interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection), and dependencies (association rule mining, sequential pattern mining). This usually involves using database techniques such as spatial indices.

The difference between data analysis and data mining is that data analysis is used to test models and hypotheses on the dataset, regardless of the amount of data.

In order to avoid lecturing you too much on what we believe you already know or has knowledge of, we feel the next thing to do is going straight to the point and showing you some of the most popular tools used in data mining which are necessarily important to your work:

• Python

While there are proprietary tools available to assist with data mining, you’ll find the best approach is to get hands-on. Python, a prerequisite tool for any data analyst, is one of the most popular open-source programming languages in the field. It’s simple to learn and extremely versatile, with various data science applications.

• H2O

H2O is an open-source machine learning platform, which aims to make AI technology accessible to everyone. It supports the most common ML algorithms and offers Auto ML functions to help users build and deploy machine learning models in a fast and simple way, even if they are not experts.

H2O can be integrated through an API, available in all major programming languages, and uses distributed in-memory computing, which makes it ideal when analyzing huge datasets.

• MonkeyLearn

MonkeyLearn is a machine learning platform that specializes in text mining. Available in a user-friendly interface, you can easily integrate MonkeyLearn with your existing tools to perform data mining in real-time. With MonkeyLearn, you can also connect your analyzed data to MonkeyLearn Studio, a customizable data visualization dashboard that makes it even easier to detect trends and patterns in your data.

• Oracle Data Mining

Oracle Data Mining is a component of Oracle Advanced Analytics that enables data analysts to build and implement predictive models. It contains several data mining algorithms for tasks like classification, regression, anomaly detection, prediction, and more.

With Oracle Data Mining, you can build models that help you predict customer behavior, segment customer profiles, detect fraud, and identify the best prospects to target.

•  Orange

If you’ve been playing around with Python but haven’t quite managed to get to grips with it yet, consider Orange. An open-source toolkit, you can think of Orange as a sort of visual front-end that utilizes common data mining libraries in Python, such as NumPy and scikit-learn.

The benefit of Orange is that it allows you to carry out data mining either using Python scripts or via its graphical user interface—whichever works best for your skill level and the task at hand. This makes Orange a fantastic learning resource for data mining newcomers.

• RapidMiner

Incorporating Python and/or R in your data mining arsenal is a great goal in the long term. In the immediate term, however, you might want to explore some proprietary data mining tools. One of the most popular of these is the data science platform RapidMiner. It unifies everything from data access to preparation, clustering, predictive modeling, and more.

Its process-focused design and inbuilt machine learning algorithms make it an ideal data mining tool for those without extensive technical skills, but who nevertheless require the ability to carry out complicated tasks.

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• Weka

Weka is an open-source machine learning software with a vast collection of algorithms for data mining. It was developed by the University of Waikato, in New Zealand, and it’s written in JavaScript. It supports different data mining tasks, like preprocessing, classification, regression, clustering, and visualization, in a graphical interface that makes it easy to use. For each of these tasks, Weka provides built-in machine learning algorithms

In Conclusion: Tasks

Finally, there are 6 key tasks that data mining is used for. Below is the outline:

• Anomaly detection involves identifying deviations in a dataset. These might either represent data errors or informative outliers, depending on the context.
• Association rule learning is a machine learning technique used to identify useful correlations between variables. Banks, for instance, use this approach to identify which products customers commonly purchase together, helping inform sales strategies.
• Clustering is the task of identifying groups of records or structures within a dataset that share a common attribute, e.g. grouping a population by hair color.
• Classification involves using what you already know about a dataset to categorize new data (for instance, classifying customers based on their age range and location).
• Regression analysis highlights relationships between one or more values. Specifically, how do independent variables impact dependent variables? (e.g. the impact of age or diet on somebody’s weight).
• Summarization is the culmination of all the steps we’ve just described. It involves creating a clear, concise report of your findings, usually with visualizations.