Data Mining Lab

Parameters

Clusters (k): 3

Iteration: 0

Points: 0

Mode: kmeans

Data Mining Explorer – Patterns in Large Datasets

Data mining is the process of discovering patterns, correlations, and anomalies in large datasets using statistical and machine-learning methods. Unlike data structures (which organize how data is stored), data mining focuses on what the data means and what can be learned from it. This explorer applies real algorithms—k-means clustering, decision trees, and Apriori association rules—to sample datasets so you can watch pattern discovery unfold visually.

What you can do in this simulation

Run k-means clustering on scatter plots and adjust k to see how clusters form
Build a decision-tree classifier and observe how it splits the feature space
Mine association rules from transaction data and tune support and confidence thresholds
Visualize outlier detection with distance-based anomaly scoring
Compare model accuracy across different algorithms on the same dataset

Concepts covered

data mining · clustering · classification · association rules · k-means · decision trees

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