Impact of Varying Distance-Based Fingerprint Similarity Metrics on Affinity Propagation Clustering Performance in Received Signal Strength-Based Fingerprint Databases
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F24%3A50021675" target="_blank" >RIV/62690094:18450/24:50021675 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/10646489" target="_blank" >https://ieeexplore.ieee.org/document/10646489</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/OJSP.2024.3449816" target="_blank" >10.1109/OJSP.2024.3449816</a>
Alternative languages
Result language
angličtina
Original language name
Impact of Varying Distance-Based Fingerprint Similarity Metrics on Affinity Propagation Clustering Performance in Received Signal Strength-Based Fingerprint Databases
Original language description
The affinity propagation clustering (APC) algorithm is popular for fingerprint database clustering because it can cluster without pre-defining the number of clusters. However, the clustering performance of the APC algorithm heavily depends on the chosen fingerprint similarity metric, with distance-based metrics being the most commonly used. Despite its popularity, the APC algorithm lacks comprehensive research on how distance-based metrics affect clustering performance. This emphasizes the need for a better understanding of how these metrics influence its clustering performance, particularly in fingerprint databases. This paper investigates the impact of various distance-based fingerprint similarity metrics on the clustering performance of the APC algorithm. It identifies the best fingerprint similarity metric for optimal clustering performance for a given fingerprint database. The analysis is conducted across five experimentally generated online fingerprint databases, utilizing seven distance-based metrics: Euclidean, squared Euclidean, Manhattan, Spearman, cosine, Canberra, and Chebyshev distances. Using the silhouette score as the performance metric, the simulation results indicate that structural characteristics of the fingerprint database, such as the distribution of fingerprint vectors, play a key role in selecting the best fingerprint similarity metric. However, Euclidean and Manhattan distances are generally the preferable choices for use as fingerprint similarity metrics for the APC algorithm across most fingerprint databases, regardless of their structural characteristics. It is recommended that other factors, such as computational intensity and the presence or absence of outliers, be considered alongside the structural characteristics of the fingerprint database when choosing the appropriate fingerprint similarity metric for maximum clustering performance. Authors
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20203 - Telecommunications
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Open Journal of Signal Processing
ISSN
2644-1322
e-ISSN
2644-1322
Volume of the periodical
5
Issue of the periodical within the volume
August
Country of publishing house
US - UNITED STATES
Number of pages
10
Pages from-to
1005-1014
UT code for WoS article
001317651300001
EID of the result in the Scopus database
2-s2.0-85202732480