Article

A new framework for clustering algorithm evaluation in the domain of functional genomics

Author:

AnYuan GuoAuthors Info & Claims

SAC '04: Proceedings of the 2004 ACM symposium on Applied computing

Pages 143 - 146

https://doi.org/10.1145/967900.967931

Published: 14 March 2004 Publication History

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Abstract

Clustering algorithms are widely used in the computational analysis of microarray data. However, due to the lack of domain knowledge, it is often difficult to judge their performance. In this paper, we introduce a new framework for the evaluation of clustering algorithms in application to regulatory pathway reconstruction. A pilot study was conducted on the hierarchical clustering algorithm for which we obtained qualitative characterizations of the number of samples needed as well as the denseness of the subnetwork required to achieve accurate partition. For experimental scientists, this evaluation framework provides a method to select and calibrate clustering algorithms. It can also provide a confidence measure to the results of a clustering algorithm when certain restrictions on the experimental setup, such as the number of samples available, are known in advance.

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Cited By

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Ben Ishay RHerman MYosefy C(2018)A New Approach for Tuned Clustering AnalysisMachine Learning and Data Mining in Pattern Recognition10.1007/978-3-319-96136-1_34(436-452)Online publication date: 8-Jul-2018
https://doi.org/10.1007/978-3-319-96136-1_34
Chauhan RKaur H(2016)Predictive Analytics and Data MiningBusiness Intelligence10.4018/978-1-4666-9562-7.ch019(359-374)Online publication date: 2016
https://doi.org/10.4018/978-1-4666-9562-7.ch019
Chauhan RKaur H(2014)Predictive Analytics and Data MiningAdvances in Secure Computing, Internet Services, and Applications10.4018/978-1-4666-4940-8.ch004(73-88)Online publication date: 2014
https://doi.org/10.4018/978-1-4666-4940-8.ch004

Index Terms

A new framework for clustering algorithm evaluation in the domain of functional genomics
1. Applied computing
  1. Life and medical sciences
2. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis

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Reviews

Reviewer: Adrian Pasculescu

Clustering algorithms, a special aspect of data mining, are the focus of this paper. Its main objective is to describe a way to evaluate different clustering algorithms. The immediate application that the author has in mind is in the domain of regulatory pathway reconstruction in functional genetics. This endeavor is inspired by the difficulties encountered by researchers in judging the best clustering, especially when such methods are used in domains where very little is known. The proposed framework bases its evaluation of different clustering algorithms on how well they partition nodes of Bayesian network models of gene interactions. The main reasons given for choosing this well-studied statistical method are that it has been adopted in the reconstruction of gene regulatory and interaction networks, and captures, albeit not perfectly, the stochastic process that gene regulation is believed to be. The main contribution of the paper, according to its author, is in establishing a framework by which scientists can select and calibrate clustering algorithms. The initial studies were conducted for hierarchical clustering algorithms, using correlation as a distance metric. It is the author's intent to further extend the framework, and the study, to k-means, self-organizing maps, support vector machines, and even to time clustering analysis of time series data. The author also promises to test the validity of the findings for larger networks (those with much more than 40 nodes). To fully understand the statistical and genetic background of the paper, readers should consult the specified references. Online Computing Reviews Service

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Published In

SAC '04: Proceedings of the 2004 ACM symposium on Applied computing

March 2004

1733 pages

ISBN:1581138121

DOI:10.1145/967900

Conference Chair:
Hisham M. Haddad
Kennesaw State University
,
Program Chairs:
Andrea Omicini
Università degli Studi di Bologna a Cesena
,
Roger L. Wainwright
University of Tulsa
,
Publications Chair:
Lorie M. Liebrock
New Mexico Institute of Mining and Technology

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 14 March 2004

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SAC04: The 2004 ACM Symposium on Applied Computing

March 14 - 17, 2004

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Cited By

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Ben Ishay RHerman MYosefy C(2018)A New Approach for Tuned Clustering AnalysisMachine Learning and Data Mining in Pattern Recognition10.1007/978-3-319-96136-1_34(436-452)Online publication date: 8-Jul-2018
https://doi.org/10.1007/978-3-319-96136-1_34
Chauhan RKaur H(2016)Predictive Analytics and Data MiningBusiness Intelligence10.4018/978-1-4666-9562-7.ch019(359-374)Online publication date: 2016
https://doi.org/10.4018/978-1-4666-9562-7.ch019
Chauhan RKaur H(2014)Predictive Analytics and Data MiningAdvances in Secure Computing, Internet Services, and Applications10.4018/978-1-4666-4940-8.ch004(73-88)Online publication date: 2014
https://doi.org/10.4018/978-1-4666-4940-8.ch004

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F-statistics algorithm for gene clustering evaluation

Evaluation of hierarchical clustering algorithms for document datasets

Self-Organizing-Map Based Clustering Using a Local Clustering Validity Index

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