Data Mining is the process of extracting useful information from a large chunk of data. Just as mining involves digging through volumes of raw ingredients to get minerals, Data Mining involves digging in extensive data to find patterns that will further help organizations to minimize risk, and create profitable marketing strategies.
With the help of effective data mining techniques coupled with Machine Learning, Database Management, and Statistics, businesses are extrapolating productivity, marketing, sales, customer service, etc.
Here, we will talk about the 7 most important data mining techniques you should know and use to help you edge ahead of your competitors.
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The Most Important Data Mining Techniques
One of the basic yet most important data mining techniques is tracking patterns. It tries to unlock a pattern in the data set—an event or a trend that occurs after a specific interval. The mechanism tries to identify the pattern by which businesses learn to focus on that particular event or follow the trend.
For example, suppose an eCommerce website wants to learn which products appeal to a specific demographic. The Tracking Pattern data mining technique helps to find the area, age group, gender, and other essential information of your customers interested in those products. Also, with the help of this data, you can create a sales funnel or show relevant items to your customer. Moreover, you can understand customer demands and behavior, which will also help you in customer support.
Association follows the concept of correlation in Statistics, and it is somewhat similar to tracking patterns. However, there is a catch. This data mining technique links data to other data from the pattern-driven result. In other words, there is a relationship between two data events.
For example, suppose a customer goes to a restaurant to eat a burger and always chooses fries and soft drinks. Here, the result shows that the occurrence of one event (a customer buying a burger) is related to another event (purchasing soft drinks and fries). The Association technique generally powers the “people also bought” section.
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The Outliner Detection technique identifies the inconsistencies in a dataset and alerts businesses of these anomalies. Why is it important? Understanding the inconsistency is essentially better for predicting the future outcomes of any business objective. In other words, Outliner Detection looks for an error in an occurring event or unique data items in a dataset.
For example, suppose women especially like an item. But, at a particular time, male buyers of that item have spiked at an abnormal rate. The business might be interested if it caused this inconsistency. For example, a retail seller suddenly observes a spike in his sales at a given time and may want to analyze this abnormality. By analyzing these anomalies, businesses can plan accordingly for future-proofing their business. Moreover, they can take measures for damage control, manage risks, and offer a pleasant user experience.
The classification technique is more complex compared to the tracking pattern. This data mining technique allows businesses to collect attributes bundled into a distinct class. From there, companies can take actions relating to that data and perform specific actions.
For example, a business can identify products selling more in a specific demographic, and it can take action to keep those products stocked for that particular area. Moreover, they can analyze the purchase history of customers by which they can categorize the customers into low to high spenders. Therefore, a business can save a significant budget targeting customers. They can curate campaigns dedicated to the area, income, etc.
Clustering is a variant of the classification data mining technique. This data mining technique focuses on grouping different data items in a series based on their properties, allowing further grouping to subsets. This approach represents data in such a way that it helps businesses make more informed decisions.
Let’s further understand this with an example. Retailers with a wide variety of items of various brands may use clustering techniques to group customers who buy a specific brand, in what quantity, and how frequently. Thus, helping businesses to plan their inventory accordingly. Car insurance policy providers can use clustering models to offer customer policies based on their income and net spending.
Regression is another crucial data mining technique primarily used for data modeling and planning. It deals with any variable present in a dataset on the occurrence of another related variable. So, it informs businesses about the relationship between the variables in a dataset.
For example, depending on the location, area (sq.ft.), last sold price, and what the cost of that house should be. In a way, based on the variables such as location, area, and last traded price, a prediction of the current value of the home is done. Furthermore, city traffic depends on various factors like time, location, population, vehicles owned by the people, etc. So, through the Regression model, businesses and entities forecast an event with relating variables.
The last yet one of the most crucial data mining techniques is Prediction. This model is popular in the private and public sectors due to its sustainable impact. It examines past data samples and predicts the future outcome. The Prediction model studies the underlying pattern and helps to forecast future performance.
Suppose you are to buy an asset now. You will decide whether to accept it depending on the pricing history, rate fluctuations, growth rate, etc. Similarly, if a person applies for a loan, the bank will determine sanction only if his records have built a good credit score. The prediction model is also among the most common data mining techniques used by Retailers, Manufacturers, Financial institutions, etc.
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How do you apply these data mining techniques?
You don’t need a million-dollar set up to run these data mining techniques. A wide variety of free and paid tools allows you to use the most popular techniques. We will try to share more information on this in some other article.
Data Mining presents an effective way of using data to our advantage. The vast sample data allows us to apply the most popular data mining techniques and learn about the key elements that help grow a business. One simple thing to keep in mind is making the most out of the data by applying correct logic that transcends to accurate information that, in return, revamps the overall outlook of a business.