5 Essential Data Mining Techniques Every Analyst Should Know

Data mining focuses on how to process data and uncover patterns and trends. According to an IBM research report, data‑mining techniques have existed for a long time, but the rise of big data has made them more meaningful and popular. In just two years (2016‑2017) the world generated 90% of its data, with about 2.5 EB created daily—enough to fill ten million Blu‑ray discs.

1. Association Rules

Association rules identify relationships between two or more items to reveal patterns. For example, a supermarket can discover that customers buying strawberries often also buy whipped cream. Such rules are used in sales and marketing systems to detect common trends between products and customers, helping businesses improve efficiency and revenue.

2. Classification and Tagging

Classification assigns items to target categories or tags using multiple attributes, enabling accurate prediction of characteristics within each class. Industries use it for risk assessment—e.g., a loan company classifies applicants into low, medium, or high credit risk—or for segmenting audiences by age or social group to guide marketing strategies.

3. Clustering

Clustering groups data records together, providing a high‑level view of what is happening in a database. By clustering customers, a company can segment the market into distinct sub‑sets and tailor marketing strategies for each cluster, such as comparing purchasing patterns across clusters.

4. Decision Trees

Decision trees are used for classification or prediction. Starting from a simple question with multiple answers, each answer leads to further questions, ultimately classifying or predicting data. For instance, a decision‑tree model can analyze mobile‑carrier customers to identify churn risk, showing how many customers move between leaf nodes and enabling intuitive model interpretation.

5. Sequential Pattern Mining

Sequential pattern mining discovers trends or likely occurrences of events over time, often applied to understand user purchasing behavior. Retailers use it to decide product placement by identifying product sets bought together at specific times. IBM reports that, based on customer data, systems can automatically recommend items in a shopping cart by analyzing browsing frequency and purchase history.