- Trending Categories
- Data Structure
- Networking
- RDBMS
- Operating System
- Java
- iOS
- HTML
- CSS
- Android
- Python
- C Programming
- C++
- C#
- MongoDB
- MySQL
- Javascript
- PHP

- Selected Reading
- UPSC IAS Exams Notes
- Developer's Best Practices
- Questions and Answers
- Effective Resume Writing
- HR Interview Questions
- Computer Glossary
- Who is Who

# What is DBSCAN?

DBSCAN stands for Density-Based Spatial Clustering of Applications with Noise. It is a density based clustering algorithm. The algorithm increase regions with sufficiently high density into clusters and finds clusters of arbitrary architecture in spatial databases with noise. It represents a cluster as a maximum group of density-connected points.

The concept of density-based clustering includes a number of new definitions as follows −

The neighborhood within a radius ε of a given object is known as the εneighborhood of the object.

If the ε-neighborhood of an object includes at least a minimum number, MinPts, of objects, then the object is known as core object.

Given a set of objects, D, it can say that an object p is directly density-reachable from object q if p is inside the ε-neighborhood of q, and q is a core object.

An object p is density-reachable from object q concerning ε and MinPts in a group of objects, D, if there is a chain of objects p

_{1},..., p_{n}, where p_{1}= q and p_{n}= p including p_{i}+1 is directly density-reachable from p_{i}concerning ε and MinPts, for 1 ≤ i ≤ n, p_{i}ε D.An object p is density-linked to object q concerning ε and MinPts in a group of objects, D, if there is an object o ε D such that both p and q are density-reachable from o concerning ε and MinPts.

Density reachability is the transitive closure of direct density reachability, and this connection is asymmetric. There is only core objects are mutually density reachable. Density connectivity is a symmetric relation.

A density-based cluster is a group of density-connected objects that is maximal concerning density-reachability. Each object not included in any cluster is treated to be noise.

DBSCAN searches for clusters by testing the ε-neighborhood of every point in the database. If the ε-neighborhood of a point p includes more than MinPts, a new cluster with p as a core object is made. DBSCAN repetitively collects directly density-reachable objects from these core objects, which can contain the merge of a few density-reachable clusters. The process removes when no new point can be inserted to any cluster.

If a spatial index is used, the computational complexity of DBSCAN is O(nlogn), where n is the number of database objects. Therefore, it is O (n^{2}). With appropriate settings of the user-represented parameters ε and MinPts, the algorithm is efficient at discovering arbitrary-shaped clusters.

- Related Questions & Answers
- What is the difference between K-Means and DBSCAN?
- What is Java API and what is its use?
- What is DatabaseMetaData in JDBC? What is its significance?
- What is ResultSetMetaData in JDBC? What is its significance?
- What is Account Balance and what is its significance?
- What is Scenario Analysis and what is its importance?
- What is Baseline Security? What is its Standard Framework?
- What is caching?
- What is Virtualization?
- What is Bitmap?
- What is Bootstrap?
- What is Alexa?
- What is SQL?
- What is pipelining?
- What is CTET?