Data mining is the process of examining large volumes of data to generate new information. Intuitively, you might think that “mining” data refers to extracting new data, but it doesn’t. Data mining is more about extrapolating patterns and knowledge from the data you have already collected.
Drawing on techniques and technologies at the intersection of database management, statistics and machine learning, data mining specialists have dedicated their careers to better understanding how to process and draw conclusions from large quantities information. But what techniques do they use to achieve this? This article lists and describes the seven most important data mining methods.
Data mining techniques
Data exploration via data mining is very effective, as long as it relies on one or more of these techniques:
- Search for patterns (or patterns). One of the most fundamental techniques in data mining is learning to recognize patterns (or patterns) in your datasets. It is usually the recognition of an aberration that recurs at regular intervals, or an ebb and flow of some variable over time. For example, you may find that sales of a certain product seem to peak just before the holidays, or notice that warmer weather attracts more people to a website.
- The classification. Classification is a more complex data mining technique that requires you to group various attributes into discernible categories, which you can then use to draw further conclusions or perform a function. For example, if you’re evaluating data on the financial history and purchase history of individual customers, you can categorize it as “low,” “medium,” or “high” credit risk. You could then use these classifications to learn more about these customers.
- Association. Association is related to detecting and following patterns, but it is more specific to interrelated variables. In this case, you are looking for specific events or attributes that are strongly correlated to another event or attribute; for example, you may notice that when your customers purchase one item, they often also purchase a second related item. This is usually what is used to power the recommendation algorithms of the “people also bought” sections of online stores.
- Detection of outliers. In many cases, simply recognizing the general pattern does not provide a clear understanding of your dataset. You also need to be able to identify anomalies or outliers. For example, if your shoppers are almost exclusively male, but during a strange week in July there is a huge spike in female shoppers, you’ll want to investigate that spike and see what caused it. , so you can either replicate it or better understand your audience in the process.
- Grouping (or clustering). Clustering is very similar to classification, but it involves grouping blocks of data based on their similarities. You can choose to group different demographics of your audience into different groups, based on their disposable income or how often they buy from your store.
- Regression. Regression, used primarily as a form of planning and modeling, serves to identify the probability of a certain variable, given the presence of other variables. For example, you can use it to predict a certain price, based on other factors like availability, consumer demand, and competition. Specifically, the main purpose of regression is to help you discover the exact relationship between two (or more) variables in a data set.
- Prediction. Prediction is one of the most valuable data mining techniques because it is used to project the types of data you will see in the future. In many cases, recognizing and understanding historical trends is enough to make a fairly accurate prediction of what will happen in the future. For example, you can look at consumers’ credit history and past purchases to predict whether they will pose a credit risk in the future. Note that a regression can be used to measure the evolution of the relationship between several variables over time.
Data mining tools
Do you need the latest and greatest machine learning technology to be able to apply these techniques? Not necessarily. In fact, you can probably achieve cutting-edge data mining operations with relatively modest database systems and simple tools, which almost every business has. For example, SQL Server users have long relied on SQL Server Data Tools (SSDT), whose services are now spread across multiple Azure Analytics services in the cloud.
You can always create your own tools, but open source solutions can also serve as the basis for doing this work. This is the case of the Apache Mahout project, a linear algorithm framework based on a specific domain language inspired by Scala. Mahout allows data scientists to deploy regression, clustering and recommendation models to perform this data mining. Knime, based on Java is also well equipped to explore data. Scikit-Learn, which combines Scypy, Matpotlib and Numpy, is very popular with data scientists familiar with Python. Rattle or Madlib are rather advanced, but Orange offers modeling functionality through a visual and low-code interface.
Whatever your approach, data mining is the best collection of techniques you have for getting the most out of the data you’ve already collected. As long as you apply the right logic and ask the right questions, you can draw conclusions that can transform your business.
This article originally appeared in the columns of DataScienceCentral.com, owned by Techtarget, which also owns MagIT.