Data Mining MCQs

A query answers a predefined question about stored data.
Data mining often explores the data to uncover relationships not specified in advance.

Binary attributes take two values such as yes/no or 0/1.
They may be symmetric or asymmetric depending on whether both states are equally important.

KDD refers to the broader process of turning raw data into useful knowledge.
Data mining is one central step within that process.

For asymmetric attributes, the presence state is often more informative than absence.
Measures such as Jaccard similarity ignore shared zeros for this reason.

Raw data usually contain missing values, noise, and inconsistent formats.
Preprocessing prepares the data so mining algorithms can operate reliably.

A data matrix is the standard tabular representation used by many algorithms.
Each row is an object and each column is a feature.

Patterns must be judged for correctness, novelty, usefulness, and relevance.
A technically valid pattern may still have little practical value.

Each transaction contains a set of purchased items.
This representation supports frequent itemset and association rule mining.

Classification learns from labeled examples to assign observations to predefined categories.
Examples include spam versus non-spam and disease versus no disease.

Social actors are represented as nodes and relationships as edges.
Graph structure enables analysis of communities, influence, and connectivity.

Regression estimates a numeric response such as price, demand, or temperature.
It differs from classification, which predicts discrete labels.

Dimensionality counts the variables used to describe each observation.
High-dimensional data can create computational and statistical challenges.