Chameleon 2: An Improved Graph-Based Clustering Algorithm

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/68378050:_____/19:00502857

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Chameleon 2: An Improved Graph-Based Clustering Algorithm

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Chameleon 2: An Improved Graph-Based Clustering Algorithm

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název periodika

ACM Transactions on Knowledge Discovery from Data

ISSN

1556-4681

e-ISSN

1556-472X

Svazek periodika

13

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

27

Strana od-do

—

Kód UT WoS článku

000457142600010

EID výsledku v databázi Scopus

2-s2.0-85061216552