A Feasible Algorithm for Locating Concave and Convex Zones of Interval Data and Its Use in Statistics-Based Clustering View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2004-12

AUTHORS

Vladik Kreinovich, Eric J. Pauwels, Scott A. Ferson, Lev Ginzburg

ABSTRACT

Often, we need to divide n objects into clusters based on the value of a certain quantity x. For example, we can classify insects in the cotton field into groups based on their size and other geometric characteristics. Within each cluster, we usually have a unimodal distribution of x, with a probability density ρ(x) that increases until a certain value x0 and then decreases. It is therefore natural, based on ρ(x), to determine a cluster as the interval between two local minima, i.e., as a union of adjacent increasing and decreasing segments. In this paper, we describe a feasible algorithm for solving this problem. More... »

PAGES

225-232

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1023/b:numa.0000049469.02043.24

DOI

http://dx.doi.org/10.1023/b:numa.0000049469.02043.24

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1044759972


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