Data Mining Fundamentals.pdf

The course aims to find meaningful patterns in data through pre-processing, visualization, and analysis. Two overlapping subfields of computer science are machine learning and data mining, both using statistical methods.

Machine Learning and Data Mining

Machine learning: constructs and studies systems that learn from data to make predictions, such as classifying spam emails.
Data mining: finds and describes structural patterns in data, discovering unknown properties.

Key Algorithm Categories

1. Numerical Prediction: predicts numeric quantities, e.g., regression.
2. Dimensionality Reduction: reduces data features, e.g., principal component analysis.
3. Clustering: groups similar observations.
4. Classification Learning: supervised learning, e.g., Naive Bayes, decision trees, neural networks.

Intelligent Data Mining and Machine Learning

Two "theorems" indicate no single best approach:

1. No Free Lunch Theorem: all search and optimization algorithms have equal average performance over all problems.
2. Ugly Duckling Theorem: distance metrics (predicates) are critical, and careful selection is necessary for success.

Take-home Message

No single method can find reasonable patterns in all datasets. Choose algorithms carefully, considering the data and problem, and select an appropriate distance metric.