Chameleon 2: An Improved Graph-Based Clustering Algorithm

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F19%3A00328346" target="_blank" >RIV/68407700:21240/19:00328346 - isvavai.cz</a>

Alternative codes found

RIV/68378050:_____/19:00502857

Result on the web

<a href="https://doi.org/10.1145/3299876" target="_blank" >https://doi.org/10.1145/3299876</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3299876" target="_blank" >10.1145/3299876</a>

Result language

angličtina

Original language name

Chameleon 2: An Improved Graph-Based Clustering Algorithm

Original language description

Traditional clustering algorithms fail to produce human-like results when confronted with data of variable density, complex distributions, or in the presence of noise. We propose an improved graph-based clustering algorithm called Chameleon 2, which overcomes several drawbacks of state-of-the-art clustering approaches. We modified the internal cluster quality measure and added an extra step to ensure algorithm robustness. Our results reveal a significant positive impact on the clustering quality measured by Normalized Mutual Information on 32 artificial datasets used in the clustering literature. This significant improvement is also confirmed on real-world datasets. The performance of clustering algorithms such as DBSCAN is extremely parameter sensitive, and exhaustive manual parameter tuning is necessary to obtain a meaningful result. All hierarchical clustering methods are very sensitive to cutoff selection, and a human expert is often required to find the true cutoff for each clustering result. We present an automated cutoff selection method that enables the Chameleon 2 algorithm to generate high-quality clustering in autonomous mode.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2019

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

ACM Transactions on Knowledge Discovery from Data

ISSN

1556-4681

e-ISSN

1556-472X

Volume of the periodical

13

Issue of the periodical within the volume

1

Country of publishing house

US - UNITED STATES

Number of pages

27

Pages from-to

—

UT code for WoS article

000457142600010

EID of the result in the Scopus database

2-s2.0-85061216552