Clustering Concepts and Techniques

These flashcards cover the fundamental concepts, methods, and metrics related to clustering techniques in data analysis.

Clustering

The use of unsupervised techniques for grouping similar objects.

Cluster

A collection of records that are similar to each other within the cluster, but dissimilar to records in other clusters.

K-Means Clustering

A clustering method that partitions a dataset into k clusters based on the closest proximity to the cluster mean (centroid).

Centroid

The center or mean of a cluster in K-means clustering.

Euclidean Distance

A measure of the straight-line distance between two points in Euclidean space.

Inertia

A measurement used in K-Means to quantify how well a dataset was clustered, calculated as the sum of squared distances from each point to its closest cluster center.

Silhouette Coefficient

A measure that assesses the quality of clustering, measuring how similar a point is to its own cluster compared to other clusters, with values ranging from -1 to 1.

Elbow Method

A technique used to determine the optimal number of clusters in K-Means by identifying the point where the Within-Cluster-Sum of Squared Errors (WSS) begins to decrease sharply.

Statistical Distance (Mahalanobis distance)

A distance measure that accounts for correlations between measurements, allowing for a more nuanced understanding of the data's structure.

Manhattan Distance

A measure of distance that computes the sum of absolute differences between points in a grid-like path.