Core point
A core point is a DBSCAN observation whose specified-radius neighborhood contains at least the required minimum number of observations, including itself.
Advanced Algorithms and Data Structures
A core point in DBSCAN is a data point whose neighborhood contains at least the required number of dataset points within the specified radius.
- How it is used
- Uses core points to decide which DBSCAN points expand clusters and which nearby points merely join them.
- How deep it goes
- Defines the neighborhood threshold, develops the expansion rule, and relates its range-query implementation to conditional running-time bounds.
- Examples
- Illustrates a minPoints threshold of 3, a DBSCAN expansion loop, and border-point handling across adjacent shards.
- What you can do after
- Build it from scratch by counting ε-neighbors, expanding only from core points, and checking shard margins when distributing DBSCAN.
Read how Advanced Algorithms and Data Structures explains core point →
Data Without Labels
A core point is a DBSCAN observation having at least minPts points, including itself, within epsilon distance.
- How it is used
- Uses local point density to identify the building blocks of DBSCAN clusters.
- How deep it goes
- Defines the ε and minPts test and links it to density, but does not detail the full clustering procedure.
- Examples
- Illustrates core points with Figure 2.29 and DBSCAN configurations using alternative eps and min_samples values.
- What you can do after
- Evaluate whether observations satisfy a DBSCAN neighborhood density threshold.
Related concepts
- DBSCANused by · DBSCAN is a density-based clustering algorithm that groups observations in dense regions and identifies observations in lower-density regions as noise or outliers.
- pointkind of · A point is a zero-dimensional location in a geometric or coordinate space represented by an ordered set of coordinate values.
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