A methodology for analyzing case retrieval from a clustered case memory

Albert Fornells; Elisabet Golobardes; Josep Maria Martorell; Josep Maria Garrell; Núria Macià; Ester Bernadó

doi:10.1007/978-3-540-74141-1_9

A methodology for analyzing case retrieval from a clustered case memory

Albert Fornells, Elisabet Golobardes, Josep Maria Martorell, Josep Maria Garrell, Núria Macià, Ester Bernadó

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

12 Citations (Scopus)

Abstract

Case retrieval from a clustered case memory consists in finding out the clusters most similar to the new input case, and then retrieving the cases from them. Although the computational time is improved, the accuracy rate may be degraded if the clusters are not representative enough due to data geometry. This paper proposes a methodology for allowing the expert to analyze the case retrieval strategies from a clustered case memory according to the required computational time improvement and the maximum accuracy reduction accepted. The mechanisms used to assess the data geometry are the complexity measures. This methodology is successfully tested on a case memory organized by a Self-Organization Map.

Original language	English
Title of host publication	Case-Based Reasoning Research and Development - 7th International Conference on Case-Based Reasoning, ICCBR 2007, Proceedings
Publisher	Springer Verlag
Pages	122-136
Number of pages	15
ISBN (Print)	9783540741381
DOIs	https://doi.org/10.1007/978-3-540-74141-1_9
Publication status	Published - 2007
Event	7th International Conference on Case-Based Reasoning, ICCBR 2007 - Belfast, Northern Ireland, United Kingdom Duration: 13 Aug 2007 → 16 Aug 2007

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	4626 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	7th International Conference on Case-Based Reasoning, ICCBR 2007
Country/Territory	United Kingdom
City	Belfast, Northern Ireland
Period	13/08/07 → 16/08/07

Keywords

Case memory organization
Case retrieval
Complexity measures
Self-organization maps
Soft casebased reasoning

Access to Document

10.1007/978-3-540-74141-1_9

Cite this

Fornells, A., Golobardes, E., Martorell, J. M., Garrell, J. M., Macià, N., & Bernadó, E. (2007). A methodology for analyzing case retrieval from a clustered case memory. In Case-Based Reasoning Research and Development - 7th International Conference on Case-Based Reasoning, ICCBR 2007, Proceedings (pp. 122-136). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4626 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-540-74141-1_9

Fornells, Albert ; Golobardes, Elisabet ; Martorell, Josep Maria et al. / A methodology for analyzing case retrieval from a clustered case memory. Case-Based Reasoning Research and Development - 7th International Conference on Case-Based Reasoning, ICCBR 2007, Proceedings. Springer Verlag, 2007. pp. 122-136 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "A methodology for analyzing case retrieval from a clustered case memory",

abstract = "Case retrieval from a clustered case memory consists in finding out the clusters most similar to the new input case, and then retrieving the cases from them. Although the computational time is improved, the accuracy rate may be degraded if the clusters are not representative enough due to data geometry. This paper proposes a methodology for allowing the expert to analyze the case retrieval strategies from a clustered case memory according to the required computational time improvement and the maximum accuracy reduction accepted. The mechanisms used to assess the data geometry are the complexity measures. This methodology is successfully tested on a case memory organized by a Self-Organization Map.",

keywords = "Case memory organization, Case retrieval, Complexity measures, Self-organization maps, Soft casebased reasoning",

author = "Albert Fornells and Elisabet Golobardes and Martorell, {Josep Maria} and Garrell, {Josep Maria} and N{\'u}ria Maci{\`a} and Ester Bernad{\'o}",

year = "2007",

doi = "10.1007/978-3-540-74141-1_9",

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booktitle = "Case-Based Reasoning Research and Development - 7th International Conference on Case-Based Reasoning, ICCBR 2007, Proceedings",

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Fornells, A , Golobardes, E, Martorell, JM, Garrell, JM, Macià, N & Bernadó, E 2007, A methodology for analyzing case retrieval from a clustered case memory. in Case-Based Reasoning Research and Development - 7th International Conference on Case-Based Reasoning, ICCBR 2007, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4626 LNAI, Springer Verlag, pp. 122-136, 7th International Conference on Case-Based Reasoning, ICCBR 2007, Belfast, Northern Ireland, United Kingdom, 13/08/07. https://doi.org/10.1007/978-3-540-74141-1_9

A methodology for analyzing case retrieval from a clustered case memory. / Fornells, Albert ; Golobardes, Elisabet; Martorell, Josep Maria et al.
Case-Based Reasoning Research and Development - 7th International Conference on Case-Based Reasoning, ICCBR 2007, Proceedings. Springer Verlag, 2007. p. 122-136 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4626 LNAI).

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

TY - GEN

T1 - A methodology for analyzing case retrieval from a clustered case memory

AU - Fornells, Albert

AU - Golobardes, Elisabet

AU - Martorell, Josep Maria

AU - Garrell, Josep Maria

AU - Macià, Núria

AU - Bernadó, Ester

PY - 2007

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N2 - Case retrieval from a clustered case memory consists in finding out the clusters most similar to the new input case, and then retrieving the cases from them. Although the computational time is improved, the accuracy rate may be degraded if the clusters are not representative enough due to data geometry. This paper proposes a methodology for allowing the expert to analyze the case retrieval strategies from a clustered case memory according to the required computational time improvement and the maximum accuracy reduction accepted. The mechanisms used to assess the data geometry are the complexity measures. This methodology is successfully tested on a case memory organized by a Self-Organization Map.

AB - Case retrieval from a clustered case memory consists in finding out the clusters most similar to the new input case, and then retrieving the cases from them. Although the computational time is improved, the accuracy rate may be degraded if the clusters are not representative enough due to data geometry. This paper proposes a methodology for allowing the expert to analyze the case retrieval strategies from a clustered case memory according to the required computational time improvement and the maximum accuracy reduction accepted. The mechanisms used to assess the data geometry are the complexity measures. This methodology is successfully tested on a case memory organized by a Self-Organization Map.

KW - Case memory organization

KW - Case retrieval

KW - Complexity measures

KW - Self-organization maps

KW - Soft casebased reasoning

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Case-Based Reasoning Research and Development - 7th International Conference on Case-Based Reasoning, ICCBR 2007, Proceedings

PB - Springer Verlag

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Fornells A , Golobardes E, Martorell JM, Garrell JM, Macià N, Bernadó E. A methodology for analyzing case retrieval from a clustered case memory. In Case-Based Reasoning Research and Development - 7th International Conference on Case-Based Reasoning, ICCBR 2007, Proceedings. Springer Verlag. 2007. p. 122-136. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-74141-1_9

A methodology for analyzing case retrieval from a clustered case memory

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Keywords

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