Introduction To Data Mining And Machine Learning

These flashcards cover key concepts related to data mining and machine learning as discussed in the ADM 3308 lecture.

Data Mining

The exploration and analysis of large quantities of data to discover meaningful patterns and rules.

Machine Learning

A subfield of artificial intelligence focused on the development of algorithms that allow computers to learn from and make predictions based on data.

Customer Segmentation

The process of dividing customers into groups based on common characteristics to target marketing efforts effectively.

Association Rules

Rules that identify relationships between different items in large datasets, often used in market basket analysis.

Classification

A data mining model that involves grouping data into pre-defined classes based on attributes.

Clustering

A data mining technique that involves grouping data without pre-defined classes, identifying similar characteristics among data points.

Knowledge Discovery in Data

The non-trivial process of identifying valid, novel, potentially useful, and understandable patterns in data.

P-value

The probability that the null hypothesis is true; used in statistical hypothesis testing to measure evidence against the null hypothesis.

Null Hypothesis

A default hypothesis that states there is no significant effect or relationship between phenomena.

CRISP-DM

An acronym for Cross-Industry Standard Process for Data Mining; a data mining process model that outlines the steps in a data mining project.

Responsible AI

The practice of using artificial intelligence in a way that is ethical and considers the potential consequences and biases of AI applications.