Examining the Impact of Fingerprint Vector Size on Similarity Determination and Clustering Performance of a Pattern-Based Similarity Metric

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F25%3A50022358" target="_blank" >RIV/62690094:18450/25:50022358 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/10937090" target="_blank" >https://ieeexplore.ieee.org/document/10937090</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ACCESS.2025.3553510" target="_blank" >10.1109/ACCESS.2025.3553510</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Examining the Impact of Fingerprint Vector Size on Similarity Determination and Clustering Performance of a Pattern-Based Similarity Metric

Popis výsledku v původním jazyce

The correlation-based context similarity coefficient (CSC) metric is a pattern-based fingerprint similarity metric that has gained interest in fingerprint database clustering operations. The performance of the traditional distance-based metric in fingerprint vector similarity determination has been known to be influenced by the size of the fingerprint vector. However, as for the correlation-based CSC metric, there is no comprehensive research on how fingerprint vector size affects its performance in similarity determination and subsequently clustering performance. As such, this paper examines the impact of fingerprint vector size on the similarity determination performance of the correlation-based CSC metric with the k-medoids algorithm employed for clustering. The analysis is performed across four synthetic and two experimentally generated fingerprint databases with varying fingerprint vector sizes. The impact analysis is carried out with the clustering algorithm set to generate K is an element of [3, 5] clusters. Additionally, the results are compared against three distance-based metrics: squared Euclidean, Manhattan, and cosine. With silhouette score as the clustering performance metric, the simulation result shows that the size of the fingerprint vector influences the similarity determination performance of the correlation-based CSC metric. Additionally, the number of clusters in which the clustering algorithm is set to generate also contributes to how the correlation-based CSC metric performs in similarity determination. The similarity determination time complexity of the correlation-based CSC metric increases with fingerprint vector size, making efficient clustering more challenging as fingerprint vector sizes increase. For optimal performance, the correlation-based CSC metric is recommended for us as a similarity metric on a database with a fingerprint vector size of N &lt;= 4 and a clustering algorithm configured to generate no more than 3 clusters.

Název v anglickém jazyce

Examining the Impact of Fingerprint Vector Size on Similarity Determination and Clustering Performance of a Pattern-Based Similarity Metric

Popis výsledku anglicky

The correlation-based context similarity coefficient (CSC) metric is a pattern-based fingerprint similarity metric that has gained interest in fingerprint database clustering operations. The performance of the traditional distance-based metric in fingerprint vector similarity determination has been known to be influenced by the size of the fingerprint vector. However, as for the correlation-based CSC metric, there is no comprehensive research on how fingerprint vector size affects its performance in similarity determination and subsequently clustering performance. As such, this paper examines the impact of fingerprint vector size on the similarity determination performance of the correlation-based CSC metric with the k-medoids algorithm employed for clustering. The analysis is performed across four synthetic and two experimentally generated fingerprint databases with varying fingerprint vector sizes. The impact analysis is carried out with the clustering algorithm set to generate K is an element of [3, 5] clusters. Additionally, the results are compared against three distance-based metrics: squared Euclidean, Manhattan, and cosine. With silhouette score as the clustering performance metric, the simulation result shows that the size of the fingerprint vector influences the similarity determination performance of the correlation-based CSC metric. Additionally, the number of clusters in which the clustering algorithm is set to generate also contributes to how the correlation-based CSC metric performs in similarity determination. The similarity determination time complexity of the correlation-based CSC metric increases with fingerprint vector size, making efficient clustering more challenging as fingerprint vector sizes increase. For optimal performance, the correlation-based CSC metric is recommended for us as a similarity metric on a database with a fingerprint vector size of N &lt;= 4 and a clustering algorithm configured to generate no more than 3 clusters.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20203 - Telecommunications

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

IEEE Access

ISSN

2169-3536

e-ISSN

2169-3536

Svazek periodika

13

Číslo periodika v rámci svazku

March

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

9

Strana od-do

51660-51668

Kód UT WoS článku

001455525600016

EID výsledku v databázi Scopus

2-s2.0-105001639432