Local and global data spread based index for determining number of clusters in a dataset

Romana Riyaz; M. Arif Wani

doi:10.1109/ICMLA.2016.0115

Local and global data spread based index for determining number of clusters in a dataset

Romana Riyaz
, M. Arif Wani

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

27 Scopus citations

Abstract

Most of the clustering algorithms are sensitive to the input parameters and produce different clustering results for different input parameters for same datasets. A number of methods and indices have been proposed for validating results of a clustering process. The most commonly used approaches for cluster validation are based on internal indices. In this paper, we propose a new cluster validity index (AR_Svread index) for the purpose of cluster validation and determining number of clusters present in a dataset. Local and global data spread based approach is proposed to measure the compactness of a cluster. A distinctness measure that is based on a penalty function is incorporated in the proposed index. We conduct a thorough comparison of five commonly known indices with the proposed index and provide a summary of experimental performance of different indices. Experimental results show that the proposed new index performs better than the commonly known indices.

Original language	English
Title of host publication	Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	651-656
Number of pages	6
ISBN (Electronic)	9781509061662
DOIs	https://doi.org/10.1109/ICMLA.2016.0115
State	Published - 31 Jan 2017
Externally published	Yes
Event	15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016 - Anaheim, United States Duration: 18 Dec 2016 → 20 Dec 2016

Publication series

Name	Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016

Conference

Conference	15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016
Country/Territory	United States
City	Anaheim
Period	18/12/16 → 20/12/16

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

Keywords

Cluster validity
Clustering
Compactness measure of clusters
Distinctness measure of clusters
Number of clusters in a dataset

ASJC Scopus subject areas

Computer Science Applications
Artificial Intelligence
Computer Networks and Communications

Access to Document

10.1109/ICMLA.2016.0115

Cite this

Riyaz, R., & Wani, M. A. (2017). Local and global data spread based index for determining number of clusters in a dataset. In Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016 (pp. 651-656). Article 7838219 (Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICMLA.2016.0115

Riyaz, Romana ; Wani, M. Arif. / Local and global data spread based index for determining number of clusters in a dataset. Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 651-656 (Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016).

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abstract = "Most of the clustering algorithms are sensitive to the input parameters and produce different clustering results for different input parameters for same datasets. A number of methods and indices have been proposed for validating results of a clustering process. The most commonly used approaches for cluster validation are based on internal indices. In this paper, we propose a new cluster validity index (ARSvread index) for the purpose of cluster validation and determining number of clusters present in a dataset. Local and global data spread based approach is proposed to measure the compactness of a cluster. A distinctness measure that is based on a penalty function is incorporated in the proposed index. We conduct a thorough comparison of five commonly known indices with the proposed index and provide a summary of experimental performance of different indices. Experimental results show that the proposed new index performs better than the commonly known indices.",

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Riyaz, R & Wani, MA 2017, Local and global data spread based index for determining number of clusters in a dataset. in Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016., 7838219, Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016, Institute of Electrical and Electronics Engineers Inc., pp. 651-656, 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016, Anaheim, United States, 18/12/16. https://doi.org/10.1109/ICMLA.2016.0115

Local and global data spread based index for determining number of clusters in a dataset. / Riyaz, Romana; Wani, M. Arif.
Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016. Institute of Electrical and Electronics Engineers Inc., 2017. p. 651-656 7838219 (Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Wani, M. Arif

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N2 - Most of the clustering algorithms are sensitive to the input parameters and produce different clustering results for different input parameters for same datasets. A number of methods and indices have been proposed for validating results of a clustering process. The most commonly used approaches for cluster validation are based on internal indices. In this paper, we propose a new cluster validity index (ARSvread index) for the purpose of cluster validation and determining number of clusters present in a dataset. Local and global data spread based approach is proposed to measure the compactness of a cluster. A distinctness measure that is based on a penalty function is incorporated in the proposed index. We conduct a thorough comparison of five commonly known indices with the proposed index and provide a summary of experimental performance of different indices. Experimental results show that the proposed new index performs better than the commonly known indices.

AB - Most of the clustering algorithms are sensitive to the input parameters and produce different clustering results for different input parameters for same datasets. A number of methods and indices have been proposed for validating results of a clustering process. The most commonly used approaches for cluster validation are based on internal indices. In this paper, we propose a new cluster validity index (ARSvread index) for the purpose of cluster validation and determining number of clusters present in a dataset. Local and global data spread based approach is proposed to measure the compactness of a cluster. A distinctness measure that is based on a penalty function is incorporated in the proposed index. We conduct a thorough comparison of five commonly known indices with the proposed index and provide a summary of experimental performance of different indices. Experimental results show that the proposed new index performs better than the commonly known indices.

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KW - Clustering

KW - Compactness measure of clusters

KW - Distinctness measure of clusters

KW - Number of clusters in a dataset

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Riyaz R, Wani MA. Local and global data spread based index for determining number of clusters in a dataset. In Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016. Institute of Electrical and Electronics Engineers Inc. 2017. p. 651-656. 7838219. (Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016). doi: 10.1109/ICMLA.2016.0115

Local and global data spread based index for determining number of clusters in a dataset

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this