Data Mining Examples

Su Subhalakshmi Chandran Updated 03 Nov 2026
3 min read

Most of the real-life examples commonly listed here — retail market basket analysis, telecom churn prediction, banking fraud detection, e-commerce recommendations, and healthcare diagnosis support — are covered with their own concrete worked examples in the lesson on data mining applications, the lesson on the strategic value of data mining, and the lesson on use of data mining. Rather than listing the same industries a fourth time, this lesson works through the two examples from that list that genuinely aren't covered elsewhere: predictive policing and dynamic (adaptive) pricing.

Predictive Policing: Clustering and Classification Applied to Crime Data

Crime-prevention applications typically combine two of the core tasks covered elsewhere in this series. Clustering algorithms — particularly density-based methods, covered in the lesson on grid-based methods and throughout the partition algorithm lessons — identify geographic and temporal hotspots: areas and time windows where reported incidents cluster more densely than a random distribution of the same total incident count would predict. Classification models trained on historical incident records then estimate the relative risk of different areas going forward.

This is also one of the clearest illustrations of why the ethical constraints covered in the lesson on the challenges of data mining are not an abstract afterthought. A model trained on historical arrest data doesn't only learn where crime occurred — it also learns wherever historical policing effort itself was unevenly distributed across neighborhoods, and a model built on that history can feed the same imbalance directly back into future deployment decisions, reinforcing it rather than correcting it. Any organization deploying this kind of model needs the same bias-auditing discipline described in that lesson, applied with particular care given how directly the output affects individual people.

Dynamic (Adaptive) Pricing: Regression Applied to Real-Time Demand

Ride-hailing and airline pricing systems adjust price in real time based on the relationship between current demand and available supply — a regression problem in the same technical sense covered in the lesson on categories of functions in data mining, where a continuous output (the price or a price multiplier) is predicted from continuous inputs. A simplified illustration of the underlying logic — not any specific company's actual formula, which is proprietary and considerably more complex — shows why the idea is still recognizably a data mining application:

Baseline fare = $10.00
Riders requesting a trip = 120
Available drivers = 40
Demand-to-supply ratio = 120 / 40 = 3.0

Simplified surge multiplier = 1 + (ratio - 1) x 0.5
                             = 1 + (3.0 - 1) x 0.5
                             = 1 + 1.0
                             = 2.0
Adjusted fare = $10.00 x 2.0 = $20.00

In a production system, the model would take in additional inputs — time of day, weather, historical demand at that location — and would have been trained on historical trip data rather than using a single fixed formula, but the core idea is unchanged: a numeric output learned from data, adjusted continuously as the input conditions change, rather than a fixed price set once and left alone.

Everything Else, Already Covered

  • Retail and market basket analysis (which products are bought together) — covered with a full support/confidence/lift worked example in the lesson on association rule mining.
  • Telecom customer churn — covered in the lesson on data mining applications.
  • Banking, finance, and fraud detection — covered with a hand-verified z-score worked example in the lesson on data mining applications.
  • Marketing, customer segmentation, and recommendation systems — covered in the lesson on data mining for business intelligence and the lesson on data mining projects.
  • Healthcare, agriculture, and scientific research — covered in the lesson on data mining applications.
  • General cost and efficiency benefits — covered with a concrete ROI calculation in the lesson on the strategic value of data mining.

Repeating any of these a third or fourth time with the same one-line description would not teach anything the worked examples in those lessons don't already teach more precisely.

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