Data Mining MCQs

Clustering is an unsupervised task that searches for natural groupings in data.
No predefined class labels are required.

Association analysis identifies co-occurrence patterns such as products purchased together.
Its outputs often include frequent itemsets and implication-style rules.

Anomaly detection searches for rare or unusual cases.
It is widely used in fraud detection, intrusion detection, and equipment monitoring.

The problem has no predefined customer classes.
Clustering can discover groups with similar purchasing or engagement patterns.

The historical labels provide target outcomes for model training.
The model learns to predict the known class for future transactions.

Semi-supervised methods exploit unlabeled structure while learning from limited labels.
This is useful when labeling is expensive but raw data are abundant.

Nominal values distinguish categories without ranking them.
Examples include color, country, and product type.

Ordinal values have a meaningful ranking such as low, medium, and high.
The distance between adjacent categories is not necessarily equal.