Python Machine Learning by Sebastian Raschka & Vahid Mirjalili

Author:Sebastian Raschka & Vahid Mirjalili
Language: eng
Format: epub
Tags: COM051360 - COMPUTERS / Programming Languages / Python, COM044000 - COMPUTERS / Neural Networks, COM042000 - COMPUTERS / Natural Language Processing
Publisher: Packt
Published: 2019-12-08T12:44:00+00:00

As you can see, in this pruned clustering hierarchy, label ID_3 was assigned to the same cluster as ID_0 and ID_4, as expected.

Locating regions of high density via DBSCAN

Although we can't cover the vast amount of different clustering algorithms in this chapter, let's at least include one more approach to clustering: density-based spatial clustering of applications with noise (DBSCAN), which does not make assumptions about spherical clusters like k-means, nor does it partition the dataset into hierarchies that require a manual cut-off point. As its name implies, density-based clustering assigns cluster labels based on dense regions of points. In DBSCAN, the notion of density is defined as the number of points within a specified radius, .

According to the DBSCAN algorithm, a special label is assigned to each example (data point) using the following criteria:

A point is considered a core point if at least a specified number (MinPts) of neighboring points fall within the specified radius, .

A border point is a point that has fewer neighbors than MinPts within ε, but lies within the radius of a core point.

All other points that are neither core nor border points are considered noise points.

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