Naslov
DBSCAN-like clustering method for various data densities

Autori
Scitovski, Rudolf ; Sabo, Kristian

Izvornik
Pattern analysis and applications (1433-7541) 23 (2020); 541-554

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
Clustering, DBSCAN, Incremental algorithm, Various data densities, Clusters merging, Least Squares distance-like function

Sažetak
In this paper, we propose a modification of the well-known DBSCAN algorithm, which recognizes clusters with various data densities in a given set of data points $A = {; ; a^i in R^n : i = 1, ldots , m}; ; $. First, we define the parameter $MinPts = floor ln |A| floor$ and after that, by using a standard procedure from DBSCAN algorithm, for each $a in A$ we determine radius $epsilon_a$ of the circle containing $MinPts$ elements from the set $A$. We group the set of all these radii into the most appropriate number $(t)$ of clusters by using Least Square distance-like function applying {; ; tt SymDIRECT}; ; or {; ; tt SepDIRECT}; ; algorithm. In that way we obtain parameters $epsilon_1 > · · · > epsilon_t$. Furthermore, for parameters ${; ; MinPts, epsilon_1}; ; we construct a partition starting with one cluster and then add new clusters for as long as the isolated groups of at least $MinPts$ data points in some circle with radius $epsilon_1$ exist. We follow a similar procedure for other parameters $epsilon_2, ldots, , epsilon_t$. After the implementation of the algorithm, a larger number of clusters appear than can be expected in the optimal partition. Along with defined criteria, some of them are merged by applying a merging process for which a detailed algorithm has been written. Compared to the standard DBSCAN algorithm, we show an obvious advantage for the case of data with various densities.

Izvorni jezik
Engleski

Znanstvena područja
Matematika

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