Decoupling of clustering and classification steps in a cluster-based classification

Ray R. Hashemi; Mahmood Bahar; Charla R. Childers; Alexander Tyler

doi:10.1109/ICMLA.2005.20

Decoupling of clustering and classification steps in a cluster-based classification

Ray R. Hashemi, Mahmood Bahar, Charla R. Childers, Alexander Tyler

Computer Science

Research output: Contribution to book or proceeding › Conference article › peer-review

Abstract

The application of cluster analysis in the "classification" area is well known. Such application takes place in two steps: "clustering" and "classification". In the clustering step, the objects of a training set are clustered using a cluster technique, Q. The outcome is a set of clusters, C. Each cluster, ci, is assigned a class label, ki, which reflects the common features of the objects in ci. The ki is a member of set K. In the classification step, a new object from a test set is assigned to one of the clusters in C using the Q, C, and K of the former step, The goal of this research effort is two fold: (I) introducing a methodology for decoupling "clustering" and "classification" steps and (2) establishing the validity of the proposed methodology by comparing its classification performance with the performance of the Rough Sets approach, and Desciminant Analysis.

Original language	English
Title of host publication	Proceedings - ICMLA 2005
Subtitle of host publication	Fourth International Conference on Machine Learning and Applications
Publisher	IEEE Computer Society
Pages	285-292
Number of pages	8
ISBN (Print)	0769524958, 9780769524955
DOIs	https://doi.org/10.1109/ICMLA.2005.20
State	Published - 2005
Event	ICMLA 2005: 4th International Conference on Machine Learning and Applications - Los Angeles, CA, United States Duration: Dec 15 2005 → Dec 17 2005

Publication series

Name	Proceedings - ICMLA 2005: Fourth International Conference on Machine Learning and Applications
Volume	2005

Conference

Conference	ICMLA 2005: 4th International Conference on Machine Learning and Applications
Country/Territory	United States
City	Los Angeles, CA
Period	12/15/05 → 12/17/05

Scopus Subject Areas

General Engineering

Keywords

Clustering
Decoupling clustering and classification processes
Extended self-organizing map (ESOM)
Learning
Rule extraction from clusters
Rule generation

Access to Document

10.1109/ICMLA.2005.20

Cite this

Hashemi, R. R., Bahar, M., Childers, C. R., & Tyler, A. (2005). Decoupling of clustering and classification steps in a cluster-based classification. In Proceedings - ICMLA 2005: Fourth International Conference on Machine Learning and Applications (pp. 285-292). Article 1607464 (Proceedings - ICMLA 2005: Fourth International Conference on Machine Learning and Applications; Vol. 2005). IEEE Computer Society. https://doi.org/10.1109/ICMLA.2005.20

Hashemi, Ray R. ; Bahar, Mahmood ; Childers, Charla R. et al. / Decoupling of clustering and classification steps in a cluster-based classification. Proceedings - ICMLA 2005: Fourth International Conference on Machine Learning and Applications. IEEE Computer Society, 2005. pp. 285-292 (Proceedings - ICMLA 2005: Fourth International Conference on Machine Learning and Applications).

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title = "Decoupling of clustering and classification steps in a cluster-based classification",

abstract = "The application of cluster analysis in the {"}classification{"} area is well known. Such application takes place in two steps: {"}clustering{"} and {"}classification{"}. In the clustering step, the objects of a training set are clustered using a cluster technique, Q. The outcome is a set of clusters, C. Each cluster, ci, is assigned a class label, ki, which reflects the common features of the objects in ci. The ki is a member of set K. In the classification step, a new object from a test set is assigned to one of the clusters in C using the Q, C, and K of the former step, The goal of this research effort is two fold: (I) introducing a methodology for decoupling {"}clustering{"} and {"}classification{"} steps and (2) establishing the validity of the proposed methodology by comparing its classification performance with the performance of the Rough Sets approach, and Desciminant Analysis.",

keywords = "Clustering, Decoupling clustering and classification processes, Extended self-organizing map (ESOM), Learning, Rule extraction from clusters, Rule generation",

author = "Hashemi, \{Ray R.\} and Mahmood Bahar and Childers, \{Charla R.\} and Alexander Tyler",

year = "2005",

doi = "10.1109/ICMLA.2005.20",

language = "English",

isbn = "0769524958",

series = "Proceedings - ICMLA 2005: Fourth International Conference on Machine Learning and Applications",

publisher = "IEEE Computer Society",

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address = "United States",

note = "ICMLA 2005: 4th International Conference on Machine Learning and Applications ; Conference date: 15-12-2005 Through 17-12-2005",

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Hashemi, RR, Bahar, M, Childers, CR & Tyler, A 2005, Decoupling of clustering and classification steps in a cluster-based classification. in Proceedings - ICMLA 2005: Fourth International Conference on Machine Learning and Applications., 1607464, Proceedings - ICMLA 2005: Fourth International Conference on Machine Learning and Applications, vol. 2005, IEEE Computer Society, pp. 285-292, ICMLA 2005: 4th International Conference on Machine Learning and Applications, Los Angeles, CA, United States, 12/15/05. https://doi.org/10.1109/ICMLA.2005.20

Decoupling of clustering and classification steps in a cluster-based classification. / Hashemi, Ray R.; Bahar, Mahmood; Childers, Charla R. et al.
Proceedings - ICMLA 2005: Fourth International Conference on Machine Learning and Applications. IEEE Computer Society, 2005. p. 285-292 1607464 (Proceedings - ICMLA 2005: Fourth International Conference on Machine Learning and Applications; Vol. 2005).

Research output: Contribution to book or proceeding › Conference article › peer-review

TY - GEN

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N2 - The application of cluster analysis in the "classification" area is well known. Such application takes place in two steps: "clustering" and "classification". In the clustering step, the objects of a training set are clustered using a cluster technique, Q. The outcome is a set of clusters, C. Each cluster, ci, is assigned a class label, ki, which reflects the common features of the objects in ci. The ki is a member of set K. In the classification step, a new object from a test set is assigned to one of the clusters in C using the Q, C, and K of the former step, The goal of this research effort is two fold: (I) introducing a methodology for decoupling "clustering" and "classification" steps and (2) establishing the validity of the proposed methodology by comparing its classification performance with the performance of the Rough Sets approach, and Desciminant Analysis.

AB - The application of cluster analysis in the "classification" area is well known. Such application takes place in two steps: "clustering" and "classification". In the clustering step, the objects of a training set are clustered using a cluster technique, Q. The outcome is a set of clusters, C. Each cluster, ci, is assigned a class label, ki, which reflects the common features of the objects in ci. The ki is a member of set K. In the classification step, a new object from a test set is assigned to one of the clusters in C using the Q, C, and K of the former step, The goal of this research effort is two fold: (I) introducing a methodology for decoupling "clustering" and "classification" steps and (2) establishing the validity of the proposed methodology by comparing its classification performance with the performance of the Rough Sets approach, and Desciminant Analysis.

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T3 - Proceedings - ICMLA 2005: Fourth International Conference on Machine Learning and Applications

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Hashemi RR, Bahar M, Childers CR, Tyler A. Decoupling of clustering and classification steps in a cluster-based classification. In Proceedings - ICMLA 2005: Fourth International Conference on Machine Learning and Applications. IEEE Computer Society. 2005. p. 285-292. 1607464. (Proceedings - ICMLA 2005: Fourth International Conference on Machine Learning and Applications). doi: 10.1109/ICMLA.2005.20

Decoupling of clustering and classification steps in a cluster-based classification

Abstract

Publication series

Conference

Scopus Subject Areas

Keywords

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