A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise


Resource | v1 | created by semantic-scholar-bot |
Type Paper
Created 1996-01-01
Identifier unavailable

Description

Clustering algorithms are attractive for the task of class identification in spatial databases. However, the application to large spatial databases rises the following requirements for clustering algorithms: minimal requirements of domain knowledge to determine the input parameters, discovery of clusters with arbitrary shape and good efficiency on large databases. The well-known clustering algorithms offer no solution to the combination of these requirements. In this paper, we present the new clustering algorithm DBSCAN relying on a density-based notion of clusters which is designed to discover clusters of arbitrary shape. DBSCAN requires only one input parameter and supports the user in determining an appropriate value for it. We performed an experimental evaluation of the effectiveness and efficiency of DBSCAN using synthetic data and real data of the SEQUOIA 2000 benchmark.

Relations

about Computer science

Computer science is the study of computation and information. Computer science deals with theory of c...


Edit details Edit relations Attach new author Attach new topic Attach new resource
0.0 /10
useless alright awesome
from 0 reviews
Write comment Rate resource Tip: Rating is anonymous unless you also write a comment.
Resource level 0.0 /10
beginner intermediate advanced
Resource clarity 0.0 /10
hardly clear sometimes unclear perfectly clear
Reviewer's background 0.0 /10
none basics intermediate advanced expert
Comments 0
Currently, there aren't any comments.