Introduction To Data Mining And Machine Learning

These flashcards cover key concepts related to Data Mining and Machine Learning, including definitions of terms, processes, and ethical considerations.

Data Mining

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

Machine Learning

A subset of artificial intelligence that enables systems to learn from data and improve automatically through experience.

Market Basket Analysis

A data mining technique used to understand the purchase behavior of customers by discovering associations between different items.

Customer Segmentation

The process of dividing customers into groups based on common characteristics for targeted marketing.

Association Rules

A data mining technique that identifies sets of items that frequently co-occur in transactions.

Classification

A data mining model that assigns items into predefined classes based on input attributes.

Clustering

A data mining technique that groups similar items together without predefined classes.

Predictive Modeling

Using historical data to predict future outcomes or trends.

Knowledge Discovery in Data (KDD)

The process of finding valid, novel, potentially useful, and understandable patterns in data.

CRISP-DM

Cross-Industry Standard Process for Data Mining, a process model for data mining projects.

Null Hypothesis

The default hypothesis that there is no significant effect or relationship; it is tested for validity.

P-value

The probability that the null hypothesis is true; used in hypothesis testing to determine statistical significance.

Responsible AI

Practices and principles focused on ensuring ethical and fair use of artificial intelligence.

Data Preparation

The process of cleaning and organizing raw data before it is used for analysis.

Data Flood

The overwhelming amount of data generated in various industries, leading to a need for effective information extraction.

Dynamic vs. Static Data

Dynamic data changes over time (e.g., databases), while static data remains constant.

Ethical Issues in Data Mining

Concerns relating to privacy, data bias, and the ethical use of data in decision-making processes.