@article {SAM:SAM11149,
	title = {Nearest-neighbors medians clustering},
	journal = {Statistical Analysis and Data Mining},
	volume = {5},
	number = {4},
	year = {2012},
	pages = {349{\textendash}362},
	publisher = {Wiley Subscription Services, Inc., A Wiley Company},
	keywords = {cluster analysis, local median, nearest neighbors, number of clusters},
	issn = {1932-1872},
	doi = {10.1002/sam.11149},
	url = {http://dx.doi.org/10.1002/sam.11149},
	author = {Pena, Daniel and Viladomat, J{\'u}lia and Zamar, Ruben}
}
@article { ISI:000275203500001,
	title = {clues: An R Package for Nonparametric Clustering Based on Local Shrinking},
	journal = {JOURNAL OF STATISTICAL SOFTWARE},
	volume = {33},
	number = {4},
	year = {2010},
	month = {FEB},
	pages = {1-16},
	publisher = {JOURNAL STATISTICAL SOFTWARE},
	type = {Article},
	address = {UCLA DEPT STATISTICS, 8130 MATH SCIENCES BLDG, BOX 951554, LOS ANGELES, CA 90095-1554 USA},
	abstract = {Determining the optimal number of clusters appears to be a persistant and controversial issue in cluster analysis. Most existing R packages targeting clustering require the user to specify the number of clusters in advance. However, if this subjectively chosen number is far from optimal, clustering may produce seriously misleading results. In order to address this vexing problem, we develop the R package clues to automate and evaluate the selection of an optimal number of clusers, which is widely applicable in the field of clustering analysis. Package clues uses two main procedures, shrinking and partitioning, to estimate an optimal number of clusters by maximizing an index function, either the CH index or the Silhouette index, rather than relying on guessing a pre-specified number. Five agreement indices (Rand index, Hubert and Arabie{\textquoteright}s adjusted Rand index, Morey and Agresti{\textquoteright}s adjusted Rand index, Fowlkes and Mallows index and Jaccard index), which measure the degree of agreement between any two partitions, are also provided in clues. In addition to numerical evidence, clues also supplies a deeper insight into the partitioning process with trajectory plots.},
	keywords = {agreement index, cluster analysis, dissimilarity measure, K-nearest neighbor},
	issn = {1548-7660},
	author = {Chang, Fang and Qiu, Weiliang and Zamar, Ruben H. and Lazarus, Ross and Wang, Xiaogang}
}
@article { ISI:000174685600010,
	title = {Estimating and depicting the structure of a distribution of random functions},
	journal = {BIOMETRIKA},
	volume = {89},
	number = {1},
	year = {2002},
	month = {MAR},
	pages = {145-158},
	publisher = {BIOMETRIKA TRUST},
	type = {Article},
	address = {UNIV COLLEGE LONDON GOWER ST-BIOMETRIKA OFFICE, LONDON WC1E 6BT, ENGLAND},
	abstract = {We suggest a nonparametric approach to making inference about the structure of distributions in a potentially infinite-dimensional space, for example a function space, and displaying information about that structure. It is suggested that the simplest way of presenting the structure is through modes and density ascent lines, the latter being the projections into the sample space of the curves of steepest ascent up the surface of a functional-data density. Modes are always points in the sample space, and ascent lines are always one-parameter structures, even when the sample space is determined by an infinite number of parameters. They are therefore relatively easily depicted. Our methodology is based on a functional form of an iterative data-sharpening algorithm.},
	keywords = {bandwidth, cluster analysis, Functional data analysis, Gaussian process, generalised Fourier expansion, Karhunen-Loeve expansion, kernel methods, line of steepest ascent, mode, nonparametric density estimation, tree diagram},
	issn = {0006-3444},
	doi = {10.1093/biomet/89.1.145},
	author = {Hall, P and Heckman, NE}
}
