Pattern discovery in melanoma domain using partitional clustering

David Vernet; Ruben Nicolas; Elisabet Golobardes; Albert Fornells; Carles Garriga; Susana Puig; Josep Malvehy

doi:10.3233/978-1-58603-925-7-323

Pattern discovery in melanoma domain using partitional clustering

David Vernet^*
, Ruben Nicolas
, Elisabet Golobardes
, Albert Fornells
, Carles Garriga
, Susana Puig
, Josep Malvehy

^*Autor corresponent d’aquest treball

Producció científica: Capítol de llibre › Contribució a congrés/conferència › Avaluat per experts

2 Cites (Scopus)

Resum

Nowadays melanoma is one of the most important cancers to study due to its social impact. This dermatologic cancer has increased its frequency and mortality during last years. In particular, mortality is around twenty percent in non early detected ones. For this reason, the aim of medical researchers is to improve the early diagnosis through a best melanoma characterization using pattern matching. This article presents a new way to create real melanoma patterns in order to improve the future treatment of the patients. The approach is a pattern discovery system based on the K-Means clustering method and validated by means of a Case-Based Classifier System.

Idioma original	Anglès
Títol de la publicació	Artificial Intelligence Research and Development. Proceedings of the 11th International Conference of the Catalan Association for Artificial Intelligence
Editor	IOS Press
Pàgines	323-330
Nombre de pàgines	8
Edició	1
ISBN (imprès)	9781586039257
DOIs	https://doi.org/10.3233/978-1-58603-925-7-323
Estat de la publicació	Publicada - 2008

Sèrie de publicacions

Nom	Frontiers in Artificial Intelligence and Applications
Nombre	1
Volum	184
ISSN (imprès)	0922-6389

SDG de les Nacions Unides

Aquest resultat contribueix als següents objectius de desenvolupament sostenible.

ODS 3 Salut i benestar

Accés al document

10.3233/978-1-58603-925-7-323

Altres arxius i enllaços

Link to publication in Scopus

Com citar-ho

Vernet, D., Nicolas, R., Golobardes, E., Fornells, A., Garriga, C., Puig, S., & Malvehy, J. (2008). Pattern discovery in melanoma domain using partitional clustering. In Artificial Intelligence Research and Development. Proceedings of the 11th International Conference of the Catalan Association for Artificial Intelligence (1 ed., pàg. 323-330). (Frontiers in Artificial Intelligence and Applications; Vol. 184, Núm. 1). IOS Press. https://doi.org/10.3233/978-1-58603-925-7-323

Vernet, David ; Nicolas, Ruben ; Golobardes, Elisabet et al. / Pattern discovery in melanoma domain using partitional clustering. Artificial Intelligence Research and Development. Proceedings of the 11th International Conference of the Catalan Association for Artificial Intelligence. 1. ed. IOS Press, 2008. pàg. 323-330 (Frontiers in Artificial Intelligence and Applications; 1).

@inproceedings{df4bd8632eef4fef84e00e8975e08382,

title = "Pattern discovery in melanoma domain using partitional clustering",

abstract = "Nowadays melanoma is one of the most important cancers to study due to its social impact. This dermatologic cancer has increased its frequency and mortality during last years. In particular, mortality is around twenty percent in non early detected ones. For this reason, the aim of medical researchers is to improve the early diagnosis through a best melanoma characterization using pattern matching. This article presents a new way to create real melanoma patterns in order to improve the future treatment of the patients. The approach is a pattern discovery system based on the K-Means clustering method and validated by means of a Case-Based Classifier System.",

keywords = "Artificial Intelligence in Medicine, Case-Based Reasoning, Clustering, Computer Aided Systems, Melanomas, Pattern Discovery",

author = "David Vernet and Ruben Nicolas and Elisabet Golobardes and Albert Fornells and Carles Garriga and Susana Puig and Josep Malvehy",

year = "2008",

doi = "10.3233/978-1-58603-925-7-323",

language = "English",

isbn = "9781586039257",

series = "Frontiers in Artificial Intelligence and Applications",

publisher = "IOS Press",

number = "1",

pages = "323--330",

booktitle = "Artificial Intelligence Research and Development. Proceedings of the 11th International Conference of the Catalan Association for Artificial Intelligence",

address = "Netherlands",

edition = "1",

}

Vernet, D, Nicolas, R, Golobardes, E , Fornells, A, Garriga, C, Puig, S & Malvehy, J 2008, Pattern discovery in melanoma domain using partitional clustering. in Artificial Intelligence Research and Development. Proceedings of the 11th International Conference of the Catalan Association for Artificial Intelligence. 1 ed., Frontiers in Artificial Intelligence and Applications, núm. 1, vol. 184, IOS Press, pàg. 323-330. https://doi.org/10.3233/978-1-58603-925-7-323

Pattern discovery in melanoma domain using partitional clustering. / Vernet, David; Nicolas, Ruben; Golobardes, Elisabet et al.
Artificial Intelligence Research and Development. Proceedings of the 11th International Conference of the Catalan Association for Artificial Intelligence. 1. ed. IOS Press, 2008. pàg. 323-330 (Frontiers in Artificial Intelligence and Applications; Vol. 184, Núm. 1).

Producció científica: Capítol de llibre › Contribució a congrés/conferència › Avaluat per experts

TY - GEN

T1 - Pattern discovery in melanoma domain using partitional clustering

AU - Vernet, David

AU - Nicolas, Ruben

AU - Golobardes, Elisabet

AU - Fornells, Albert

AU - Garriga, Carles

AU - Puig, Susana

AU - Malvehy, Josep

PY - 2008

Y1 - 2008

N2 - Nowadays melanoma is one of the most important cancers to study due to its social impact. This dermatologic cancer has increased its frequency and mortality during last years. In particular, mortality is around twenty percent in non early detected ones. For this reason, the aim of medical researchers is to improve the early diagnosis through a best melanoma characterization using pattern matching. This article presents a new way to create real melanoma patterns in order to improve the future treatment of the patients. The approach is a pattern discovery system based on the K-Means clustering method and validated by means of a Case-Based Classifier System.

AB - Nowadays melanoma is one of the most important cancers to study due to its social impact. This dermatologic cancer has increased its frequency and mortality during last years. In particular, mortality is around twenty percent in non early detected ones. For this reason, the aim of medical researchers is to improve the early diagnosis through a best melanoma characterization using pattern matching. This article presents a new way to create real melanoma patterns in order to improve the future treatment of the patients. The approach is a pattern discovery system based on the K-Means clustering method and validated by means of a Case-Based Classifier System.

KW - Artificial Intelligence in Medicine

KW - Case-Based Reasoning

KW - Clustering

KW - Computer Aided Systems

KW - Melanomas

KW - Pattern Discovery

UR - https://www.scopus.com/pages/publications/84875958739

U2 - 10.3233/978-1-58603-925-7-323

DO - 10.3233/978-1-58603-925-7-323

M3 - Conference contribution

AN - SCOPUS:84875958739

SN - 9781586039257

T3 - Frontiers in Artificial Intelligence and Applications

SP - 323

EP - 330

BT - Artificial Intelligence Research and Development. Proceedings of the 11th International Conference of the Catalan Association for Artificial Intelligence

PB - IOS Press

ER -

Vernet D, Nicolas R, Golobardes E , Fornells A, Garriga C, Puig S et al. Pattern discovery in melanoma domain using partitional clustering. In Artificial Intelligence Research and Development. Proceedings of the 11th International Conference of the Catalan Association for Artificial Intelligence. 1 ed. IOS Press. 2008. pàg. 323-330. (Frontiers in Artificial Intelligence and Applications; 1). doi: 10.3233/978-1-58603-925-7-323

Pattern discovery in melanoma domain using partitional clustering

Resum

Sèrie de publicacions

SDG de les Nacions Unides

Accés al document

Altres arxius i enllaços

Fingerprint

Com citar-ho