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.
There are a variety of free tools available for those looking to implement data mining techniques. But, choosing the right data mining tool will make all the difference when it come to generating real business results.
The process of data mining
Before jumping into the tools necessary for successful data mining, having a solid understanding of the cross-industry standard process for data mining is essential.
The process we’re referring to is CRISP-DM, which has been around since 1996 and is the most widely used and relied upon analytics process in the world. Here we’ll be covering a brief overview of the process.
The Phases of CRISP-DM
- Business understanding
- Data understanding
- Data preparation
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.
Still looking for more information on CRISP-DM? Be sure to check out our Human’s Guide to Machine Learning Projects for even more details.
Common 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.
Choosing the best data mining tool
With so many free tools available, one of the most difficult tasks is simply choosing a data mining tool that’s right for your business.
In addition to this, you need a tool that is fit to take your data mining to the next level and drive real business impact – the things that every leadership team wants to hear about: increased revenue and reduced cost!
But, it doesn’t have to be that difficult.
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 for free today!