Data mining is the process of uncovering patterns inside large sets of data to predict future outcomes. Structured data is data that is organized into columns and rows so that it can be accessed and modified efficiently.
Using a wide range of machine learning algorithms, you can use data mining approaches for a variety of use cases to increase revenues, reduce costs, and avoid risks.
However, if you are looking to analyze unstructured data (from essays, articles, computer log files, etc.), text mining is the way to go.
Data mining tools and process
Before jumping into all of the details, having a solid understanding of CRISP-DM (the cross-industry standard process for data mining) is essential.
CRISP-DM has been around since 1996 and is the most widely used and relied upon analytics process in the world.
Phases of CRISP-DM
Here’s a brief overview of the process. However, we talk about this in much greater details in our Human’s Guide to Machine Learning Projects.
- Business understanding
- Data understanding
- Data preparation
- Modeling
- Evaluation
- Deployment
The first two phases, business understanding and data understanding, are both preliminary activities. It is important to first define what you would like to know and what questions you would like to answer and then make sure that your data is centralized, reliable, accurate, and complete.
Once you’ve defined what you want to know and gathered your data, it’s time to prepare your data. This is where you can start to use data mining tools.
Data mining software can assist in data preparation, modeling, evaluation, and deployment. Data preparation includes activities like joining or reducing data sets, handling missing data, etc.
The modeling phase in data mining is when you use a mathematical algorithm to find pattern(s) that may be present in the data. This pattern is a model that can be applied to new data.
Data evaluation is the phase that will tell you how good or bad your model is. Cross-validation and testing for false positives are examples of evaluation techniques available in data mining tools. The deployment phase is the point at which you start using the results.
Key data mining algorithms
Now that we have a solid understanding of the different phases involved in a standard approach to data mining, let’s talk about the key algorithms to be aware of.
Data mining algorithms, at a high level, fall into two categories:
- Supervised learning
- Unsupervised learning
But what exactly is the difference? Supervised learning requires a known output, sometimes called a label or target. These algorithms include Naïve Bayes, Decision Tree, Neural Networks, SVMs, Logistic Regression, etc.
On the other hand, unsupervised learning algorithms do not require a predefined set of outputs but rather look for patterns or trends without any label or target. They include k-Means Clustering, Anomaly Detection, and Association Mining.
Your data mining tool delivering lightning fast business impact
One of the most difficult tasks is choosing the right data mining tool to help drive revenue, reduce costs and avoid risks. But, it doesn’t have to be that way.
RapidMiner Studio is a powerful data mining tool for rapidly building predictive models. The all-in-one tool features hundreds of data preparation and machine learning algorithms to support all your data mining projects.
Get started on your data mining project by downloading RapidMiner Studio today!