K-Means Clustering & Time Series Analysis

These flashcards cover key concepts and terminology related to K-Means Clustering and Time Series Analysis.

K-Means Clustering

An unsupervised learning algorithm that groups similar data points into K clusters based on their features.

Euclidean distance

A distance measurement that calculates the straight-line distance between two points in Euclidean space.

Convergence in K-Means

The process where the algorithm reaches a point where further iterations do not significantly change the cluster assignments or centroid positions.

Standardization

The process of normalizing the range of independent variables or features of data to ensure uniformity in K-Means clustering.

The Elbow Method

A technique used to determine the optimal number of clusters (K) by plotting the sum of squared distances versus the number of clusters.

Clusters

Groups of data points that share similar characteristics identified by the K-Means algorithm.

Time Series Data

Data collected sequentially over time at regular intervals (e.g., hourly, daily, monthly).

Trend

The long-term upward or downward movement in a time series data.

Seasonality

Regular, predictable patterns that repeat over fixed periods within time series data.

Additive Model

A time series model where seasonal variations remain constant in magnitude over time.

Multiplicative Model

A time series model where seasonal variations change proportionally with the trend.

R² in regression

A statistical measure that represents the proportion of variance for a dependent variable that's explained by an independent variable or variables in a regression model.

Autocorrelation

The correlation of a signal with a delayed copy of itself, often considered in time series analysis to detect patterns in residuals.

KNN vs K-Means

KNN is a supervised learning algorithm (classification/regression) while K-Means is an unsupervised learning algorithm (clustering).