IB Computer Science Topic A4.3.4: Clustering Techniques in Unsupervised Learning Key Definitions

The mean position of all the data points assigned to a cluster. It is a calculated position rather than an observation, so usually no data point sits exactly on it.

Clustering is a technique used to group a set of objects so that objects in the same group (or cluster) are more similar to each other than to those in other groups.

A density-based clustering algorithm that groups points packed closely together, using a neighbourhood radius epsilon and a minimum neighbour count minPts, and labels points in sparse regions as outliers. It finds clusters of any shape and does not need the number of clusters in advance.

A clustering method that repeatedly merges the two most similar clusters, building a tree of nested groupings from single points up to one whole. The number of clusters is chosen afterwards by cutting the tree at a selected level.

An unsupervised learning algorithm that splits data into k non-overlapping clusters by repeatedly assigning every point to its nearest centroid and moving each centroid to the mean of the points assigned to it. The number of clusters k is chosen by the analyst before the algorithm runs.

A clustering algorithm that repeatedly moves a sliding window to the mean position of the points inside it until the window settles on a dense region. Each point of convergence becomes a cluster centre, so the number of clusters is discovered rather than specified.

Rescaling a numeric feature into a fixed range, usually 0 to 1, so that features measured in different units contribute to a model on a comparable scale. This is a different process from normalization in relational database design.

A measure of clustering quality that compares how close each point is to the other points in its own cluster against how close it is to the nearest other cluster. It needs no labels, and values near 1 indicate tight, well separated clusters.

Deals with unlabeled data, aiming to find hidden patterns or structures.