Getting started: Introduction to data mining techniques

Data mining is the process of using statistical methods to uncover patterns and insights within large datasets. Typically, the datasets used for data mining are so large that it would take days, weeks, or months for humans to read or analyze. Consequently, data mining often involves using programs, machine learning, or artificial intelligence to do the work.

However, human analysts or database administrators often need to be involved, and you need to clean your data, so your datasets are prepared for analysis. With governed data, your data stewards need to be knowledgeable of these methods to train machines to uncover these insights and oversee their results to verify they are correct. This article is a quick guide and primer about data mining.

Industry applications of data mining

One of the most common goals in data mining is to find the relationships between variables in your data. These variables can be things like customers, inventory products, or transactions. Frequent pattern mining is a data-mining method that searches large datasets for recurring relationships.

Shopping basket analysis

The most common use case for frequent pattern mining is analyzing shopping behavior e.g., using data mining to determine what items are bought together more frequently and how often shoppers buy them together. These insights can be used to plan e-commerce websites and marketing campaigns. For example: if analysis found that toothpaste and razors are often bought together, you might offer a promo for both items or suggest one item when a customer adds the other to their cart.

Association rules

Imagine investigating the relationship between bread and egg purchases in grocery stores. Frequent pattern mining finds a pattern right away, but we can dig deeper. These patterns are described by association rules and calculated by statistics. Association rules include what percentage of transactions include bread and eggs purchased together (the Support rule), and the percentage of shoppers who bought bread who also bought eggs (Confidence percentage rule). After training the data-mining application, neural network, or algorithm to look for this pattern, it determines that 3 percent of all transactions include bread and eggs (Support rule), and 60 percent of shoppers who bought bread also bought eggs (Confidence).

You can train your machine for association rule learning; however, it’s up to data teams to decide the minimum threshold of support and confidence. When the pattern exceeds these thresholds, data miners can train the machine to report these patterns, and you can act on those insights.

Studying customer information

Classification and regression analysis are standard methods to predict customer behavior, automate the sorting of customers into demographic groups, forecast revenue, and more. Classification and regression are similar because analysts use each to predict outcomes.

Classification involves training an algorithm to predict what class or label an observation (such as a customer) will be classified as. Typically, this classification method takes the form of a decision tree. Having this information is crucial for customer relationship management: you can target messaging with information that is relevant and personalized to those groups.

Meanwhile, regression models are created to predict a numerical value, such as the revenue generated or claim amount in a healthcare setting.

Data specialists may also use a method called clustering to group customers together based on similar characteristics. Again, this method involves training an algorithm or neural network to do this work for you because the customer database may be so extensive that it would be a waste of resources to do it manually. Clustering is also an excellent method to find outliers. Outlier detection can flag fraudulent transactions or other suspicious customer behavior.

Predicting sales and revenue

Like classification and regression, time series analysis and forecasting are also methods that help predict outcomes. However, these data-mining methods are used to predict trends with financial data. Time series analysis involves analyzing regular observations or data points within a specified time range. A data-mining platform or programmer can analyze historical data and use that analysis to forecast future trends. These trends can be seasonal or cyclical product sales, which might inform companies about when to adjust inventory and ensure stores have the right number of items in stock.

Learn data mining skills

Data mining involves advanced statistics and coding knowledge. While you may be teaching machines to do the manual labor for you, you’ll need to be knowledgeable enough to teach them and spot when they’re doing something incorrectly. Beyond technical skills, data mining needs a research mindset. When you are doing data mining, you are an explorer of data and a problem-solver.

Data-mining tutorials can show you what other people have done, but your solution and your techniques are personalized to your company’s data. A great way to start learning is reading introductory resources on data science.