An Intro to Data Mining, and How it Uncovers Patterns and Trends
When we think of the word “mining,” we often associate it with searching for gold, coal, ore, and other valuable resources. Data mining, however, is much different.
Data mining searches for patterns and trends in an intelligent way. This means that data mining often utilizes technologies likemachine learning softwareand artificial intelligence (AI), as well as statistical models.
If it sounds confusing, that’s because data mining is a much more mathematical and scientific process for interpreting data. But data mining is a crucial step for helping businesses find the value that lies within all its data.
Data mining is an important subset of data science. Contrary to its name, none of data mining is about searching for data itself. As a matter of fact, all the data that’s going to be “mined” will need be gathered in a data warehouse ahead of time. Perhaps “knowledge mining” would be a better phrase than data mining, but let’s move on.
Different data mining techniques can be applied to everything from descriptive to predictive analyses. For example, data mining can search through a businesses’ historical data to see which customers are buying which products at certain times of the year, and map out ways to segment those customers.
Why would a business require customer segmentation? To test new ways of targeting its sales and marketing campaigns – which could lead to higher profits, but also point toward a potential trend or two.
Data mining is essential for finding relationships within large amounts and varieties of big data. This is why everything from business intelligence software to big data analytics programs utilize some form of data mining.
Because big data is a seemingly random pool of facts and details, a variety of data mining techniques are required to reveal different insights. Our example from earlier explains how data mining can segment customers, but data mining can also determine customer loyalty, identify risks, build predictive models, and much more.
One data mining technique is called clustering analysis, which essentially groups large amounts of data together based on their similarities. This mockup below shows what a clustering analysis may look like.
Data that is sporadically laid out on a chart can actually be grouped in strategic ways through clustering analysis. This analysis can also act as a preprocessing step – which basically means data is formatted in a way so other techniques can be easily applied.
What is it used for? There are a few ways to draw knowledge out of a clustering analysis. Insurance companies can identify groups of policy holders with high average claims. Seismologists can see the origin of earthquake activity and the strength of each earthquake, then apply that insight for designing evacuation routes.
Also known as outlier detection, this data mining technique does perhaps the opposite of clustering. Instead of searching for large groups of data that could be grouped together, anomaly detection looks for data points that are rare and outside an established group or average.
Because data is pretty random, anomalies don’t necessarily point toward a trend. Instead, data that goes against the grain could indicate something abnormal is going on and requires further analysis.
What is it used for? Anomaly detection is most commonly used in fraud detection. For example, anomaly detection can identify suspicious credit card activity and trigger a response. There is usually some level of machine learning involved in this case.
In an age where cyberattacks are more robust and common than ever, anomaly detection helps identify breaches on websites so they can be quickly resolved. This is called intrusion detection.
Looking for groups and outliers are a few ways to mine for knowledge, but another technique called association rule mining looks at how one variable relates to another.
The insight from association rule mining can help businesses identify potential correlations.
