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Data Mining exam questions and answers .
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Data Mining exam questions and answers .
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Data Mining exam questionsand answers 2025-2026
Data mining
- science of extracting useful info from large datasets
- discovering relationships between parts of a dataset
- relationships and summaries = models or patterns
- "trend" used to describe patterns that occur or change over time
- difference between data mining and machine learning
- Data mining relates to:
databases,
statistics,
AI,
machine learning,
pattern recognitions
- cyclical: starts w/ data, mining leads to discovery, leads to
action/deployment, leads to new data, and the cycle continues
Machine Learning
- process of training an algorithm on existing dataset to discover
relationships
- using result to analyze NEW data
Brainpower
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Typical Uses of Data Mining
- predicting which customers will purchase what products and when
- set insurance rates to ensure profitability
- predicting equipment failures (reducing unnecessary maintenance,
increasing uptime)
- detecting fraud
- minimizing financial risk
Data Mining Algorithms (categories)
, - 4 categories:
1. Classification: involves labeling data
2. Clustering: involved grouping data based on similarity
3. Association: involves relating data
4. Regression: involves coupling data (including finding outliers)
- supervised (provide categories/train using known outcomes) vs
unsupervised (provide algorithm, leaving it to learn on its own), semi-
supervised, self supervised
Decision trees
- classification algorithm
- machine learning methods for prediction models
- models obtained by recursively partitioning data and fitting prediction
model w/in each partition
- If the outcome is categorical (aka consists of classes) = classification
tree
- categories are name, they provide "nominal" data
- if the target variable is a numerical quantity (aka continuous) =
regression tree
- numerical quantities can be ordered (such data called ordinal)
- classification trees and regression trees = CART algorithms
Support Vector Machine (SVM)
- classification algorithm
- partitions data into two sets
- uses slicing hyperplane (multidimensional equivalent of a line)
- hyperplane maximizes gap on either side, minimizes chances of mis-
classifying data
- on either side, equidistant data points closest to hyperplane = support
vectors
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