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

^*Corresponding author for this work

Research output: Book chapter › Conference contribution › peer-review

2 Citations (Scopus)

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.

Original language	English
Title of host publication	Artificial Intelligence Research and Development. Proceedings of the 11th International Conference of the Catalan Association for Artificial Intelligence
Publisher	IOS Press
Pages	323-330
Number of pages	8
Edition	1
ISBN (Print)	9781586039257
DOIs	https://doi.org/10.3233/978-1-58603-925-7-323
Publication status	Published - 2008

Publication series

Name	Frontiers in Artificial Intelligence and Applications
Number	1
Volume	184
ISSN (Print)	0922-6389

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Artificial Intelligence in Medicine
Case-Based Reasoning
Clustering
Computer Aided Systems
Melanomas
Pattern Discovery

Access to Document

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

Cite this

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., pp. 323-330). (Frontiers in Artificial Intelligence and Applications; Vol. 184, No. 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. pp. 323-330 (Frontiers in Artificial Intelligence and Applications; 1).

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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",

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series = "Frontiers in Artificial Intelligence and Applications",

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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 edn, Frontiers in Artificial Intelligence and Applications, no. 1, vol. 184, IOS Press, pp. 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. p. 323-330 (Frontiers in Artificial Intelligence and Applications; Vol. 184, No. 1).

Research output: Book chapter › Conference contribution › peer-review

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

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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. p. 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

Abstract

Publication series

UN SDGs

Keywords

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