The key difference between classified and clustered data lies in how the data is grouped and the role of labels. Classified data is the result of classification, a supervised learning process where data points are assigned to predefined categories or classes based on labels provided by humans. The model is trained on labeled data and predicts the class of new, unseen data. So, classified data always has known labels or categories assigned to it. Clustered data, on the other hand, comes from clustering, an unsupervised learning process where data points are grouped based on their similarities without using any predefined labels. The clusters are formed by discovering inherent patterns or groupings in the data itself. Therefore, clustered data groups do not have predefined labels and the number of clusters is usually not known beforehand.
Summary of Differences
Aspect| Classified Data| Clustered Data
---|---|---
Learning Type| Supervised (requires labeled data)| Unsupervised (no labeled
data)
Data Labels| Predefined, human-created class labels| No predefined labels;
clusters formed from data patterns
Goal| Predict class membership of new data points| Discover natural groupings
or structures
Number of Groups| Known beforehand| Usually unknown and can vary
Use Cases| Spam detection, credit fraud prediction| Customer segmentation,
pattern discovery
In essence, classified data has distinct predefined categories from labeled training, while clustered data groups are discovered based on intrinsic similarities without prior labels.
