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Also known as: MinPts
The minimum number of points required to form a dense region in DBSCAN clustering.
DESCRIPTION
In DBSCAN, a point is a core point if at least MinPts neighbors exist within the ε-radius. MinPts governs cluster sensitivity and noise labeling. Higher values demand denser clusters and can reduce spurious groups. Practitioners select MinPts using k-distance plots and domain heuristics. Interplay with ε is crucial for stable clustering.
ALTERNATIVE DEFINITIONS
scikit-learn
min_samples: The number of samples in a neighborhood for a point to be considered as a core point.
Operational parameter definition linking MinPts to core points.
DBSCAN Original Paper (Ester et al., 1996)
A point p is a core point if at least MinPts points are within its ε-neighborhood.
Canonical definition from the original algorithm paper.
ELKI
DBSCAN requires two parameters, ε and MinPts; MinPts is the minimum number of points to form a dense region.
Summarizes the role of MinPts in density-based clustering.
RELATED TERMS
DBSCAN Epsilon Core Point Density Reachability k-distance Plot HDBSCAN Cluster Analysis Noise Points min_samples Data Anomaly Detection
COMPETING TERMS
Epsilon (DBSCAN)
KNOWLEDGE AREAS
TARGET AUDIENCE
Data Scientist AI Engineer MLOps Engineer Data Analyst
EXTERNAL SOURCES
- Wikipedia - DBSCAN
- scikit-learn - DBSCAN algorithm overview
- CRAN R dbscan - DBSCAN vignette in R
- ELKI - DBSCAN tutorial
TOP QUESTIONS
How does Minimum Points affect DBSCAN clustering?
Higher MinPts demands denser clusters, reducing noise but potentially merging nearby groups. Lower values increase sensitivity to small, sparse clusters.
Category: Opinion
How should I choose Minimum Points for DBSCAN?
Use k-distance plots, domain knowledge, and sensitivity tests. Many practitioners start with MinPts around dimensionality plus one.
Category: Best practices
What mistakes are common around Minimum Points?
Picking MinPts without adjusting ε often yields poor clusters. Validate on multiple scales and subsets.
Category: Mistakes
What is the relationship between Minimum Points and Epsilon?
MinPts defines density threshold while ε sets neighborhood radius. They interact to determine core points and cluster formation.
Category: Example
Can Minimum Points help with outlier detection?
Yes, points lacking enough neighbors become noise or border points, revealing anomalies.
Category: Topic
Is Minimum Points dataset-size dependent?
It depends on intrinsic density patterns, not just size. However, larger datasets often tolerate higher MinPts.
Category: Misunderstanding
What is the risk of a too-small Minimum Points?
You may identify spurious clusters and mislabel noise. Validation is essential.
Category: Risks
How does HDBSCAN relate to Minimum Points?
HDBSCAN generalizes DBSCAN parameters and often automates cluster selection, reducing manual MinPts tuning.
Category: Alternatives
Does scaling features impact Minimum Points choice?
Yes, scaling affects distances. Standardize features before tuning ε and MinPts.
Category: Requirements