Data mining is the umbrella term for the process of gathering raw data and transforming it into actionable information. Due to the dramatic growth of user-friendly data visualization tools, data mining is becoming more common for the everyday user – which makes effective data mining techniques that much more important.
There are a number of techniques business leaders and staffers should learn to hone their data mining skills – the list grows with time.
Leading Data Mining Techniques
Here are some fundamental data mining techniques that both analysts and non-analysts can apply in their operations. Remember, don’t be afraid to start small; this is a complex activity and it takes great practice.
Select The Optimal Tools
One fundamental step to make all your processes easier is selecting the right tools for data analysis. Selecting the optimal tools will not only make data mining easier to accomplish, but it assists you with maintaining larger databases. This is especially important when considering the fact that databases are growing far too large for traditional means.
Make sure you have strong data quality and data analytics tools. This ensures you have clearly presented, graphically displayed data to mine and analyze. Data quality tools in particular can help you with data cleansing, auditing, and migration.
One of the most fundamental and easy to learn data mining techniques is pattern tracking. This is the ability to spot important trends and patterns in data sets amid a large amount of random information.
In fact, every data mining technique stems from the idea of pattern tracking. Honing your pattern tracking skills can allow you to drill down on your data with more advanced techniques. Try finding patterns without any predetermined goals to practice your pattern tracking.
Association is one of the simplest data mining techniques users can leverage – it’s one of the first data mining techniques users can leverage once they’ve practiced their pattern tracking. Association boils down to simple correlation.
It is similar to pattern tracking, but leverages dependent variables. For example, in a data set of customer purchases, you might find that users who bought milk more often than not also bought cookies in the same transaction. This is a relatively fair association to make.
Association can be helpful, but could potentially misdirect users. Users should remember that correlation does not equal causation, and outside factors should optimally be considered in any data mining technique.
Classification is the process of leveraging shared characteristics to understand groups. These classifications can include age groups, customer type, or any other factor you please.
Classification’s strength is that it can get as specific as you need it to be. You can classify customers with as much information as you’re able to extrapolate. Be sure to connect with your sales and marketing team to ensure your predetermined classes are correct.
Classification is often confused with another data mining technique, clustering. As we’ll see later on, both techniques offer stark differences for businesses.
Outlier and Anomaly Detection
Anomaly detection can serve as an effective data mining technique for any analyst and non-analyst. This is the practice of tracking your data, and specifically looking for any outliers.
Anomaly detection is very effective for training business leaders and employees on correlation and causation. This is because anomalies are not inherently a bad thing.
For example, if you notice a huge spike in sales for a product that historically hasn’t done so well, don’t jump to conclusions. Make sure you’re in contact with different facets of your business, including your sales and marketing teams. These teams could give insight into why these spikes are occurring.
Clustering is very similar to classification. It is the technique of grouping clusters of data together based on similarities you’ve tracked. The primary difference between clustering and classification is that classification works with predefined classes.
Clustering does not use pre-labeled data or training sets. And because of this, it is less complex than classification. Clustering can be a very effective way to discern objects from one another. From here, you can create customer profiles and drill down on your data.
Finally, regression analysis is the technique of analyzing the relationship among all your variables. In other words, it is the practice of making predictions based on the data you currently have.
Regression analysis is the primary way data scientists and businesses identify the likelihood of any given variable.
You select the variable you’d like to analyze, or your dependent variable and the data points you believe affect that variable, or your independent variables. From there, you could leverage regression analysis to understand the exact relationship between these two data sets. Ultimately, regression analysis is the primary way users new to data mining can gain a deeper understanding of their data sets. It’s a method that goes beyond simple causation and correlation.