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Proximity-Graph-Based Tools for DNA Clustering

Proximity-Graph-Based Tools for DNA Clustering

Imad Khoury, Godfried Toussaint, Antonio Ciampi, Isadora Antoniano
Copyright: © 2009 |Pages: 9
ISBN13: 9781605660103|ISBN10: 1605660108|EISBN13: 9781605660110
DOI: 10.4018/978-1-60566-010-3.ch248
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MLA

Khoury, Imad, et al. "Proximity-Graph-Based Tools for DNA Clustering." Encyclopedia of Data Warehousing and Mining, Second Edition, edited by John Wang, IGI Global, 2009, pp. 1623-1631. https://doi.org/10.4018/978-1-60566-010-3.ch248

APA

Khoury, I., Toussaint, G., Ciampi, A., & Antoniano, I. (2009). Proximity-Graph-Based Tools for DNA Clustering. In J. Wang (Ed.), Encyclopedia of Data Warehousing and Mining, Second Edition (pp. 1623-1631). IGI Global. https://doi.org/10.4018/978-1-60566-010-3.ch248

Chicago

Khoury, Imad, et al. "Proximity-Graph-Based Tools for DNA Clustering." In Encyclopedia of Data Warehousing and Mining, Second Edition, edited by John Wang, 1623-1631. Hershey, PA: IGI Global, 2009. https://doi.org/10.4018/978-1-60566-010-3.ch248

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Abstract

Clustering is considered the most important aspect of unsupervised learning in data mining. It deals with finding structure in a collection of unlabeled data. One simple way of defining clustering is as follows: the process of organizing data elements into groups, called clusters, whose members are similar to each other in some way. Several algorithms for clustering exist (Gan, Ma, & Wu, 2007); proximity-graph-based ones, which are untraditional from the point of view of statisticians, emanate from the field of computational geometry and are powerful and often elegant (Bhattacharya, Mukherjee, & Toussaint, 2005). A proximity graph is a graph formed from a collection of elements, or points, by connecting with an edge those pairs of points that satisfy a particular neighbor relationship with each other. One key aspect of proximity-graph-based clustering techniques is that they may allow for an easy and clear visualization of data clusters, given their geometric nature. Proximity graphs have been shown to improve typical instance-based learning algorithms such as the k-nearest neighbor classifiers in the typical nonparametric approach to classification (Bhattacharya, Mukherjee, & Toussaint, 2005). Furthermore, the most powerful and robust methods for clustering turn out to be those based on proximity graphs (Koren, North, & Volinsky, 2006). Many examples have been shown where proximity-graph-based methods perform very well when traditional methods fail miserably (Zahn, 1971; Choo, Jiamthapthaksin, Chen, Celepcikay, Giusti, & Eick, 2007).

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