NDLI: Clustering around medoids based on ultrametric properties

Content Provider	IEEE Xplore Digital Library
Author	Fouchal, S. Bui, M. Lavallee, I.
Copyright Year	2012
Description	Author affiliation: Laboratoire LaISC, Ecole Pratique des Hautes Etudes (Bui, M.) \|\| Laboratoire LaISC, Université de Strasbourg (Fouchal, S.) \|\| Laboratoire LaISC, Université Paris 8 (Lavallee, I.)
Abstract	The FFUCA (Fast and Flexible Unsupervised Clustering Algorithm) is a fast clustering method based on ultrametric properties. It aggregates data in the same way as partitional methods. But, it elects representatives differently. Indeed, in FFUCA the cluster representatives are deduced from an ultrametric structure built from a sample data. This ultrametric structure gives the data behavior according to used distance. Thus the results are independent from the cluster representatives. We propose in this paper an extension named FFUCAAM to change for better the quality of clusters. Indeed, we improve the election of these representatives. We substitute them by mediods after every new aggregation. This extension increases the complexity in the average case to $O(Σ^{k}$ $m^{2})$ where k is the number of the resulting clusters and mi is the size of the cluster Ci. In fact, its computational cost is increased but it still less than $O(n^{2}),$ thus it remains applicable to large databases.
Starting Page	986
Ending Page	991
File Size	804449
Page Count	6
File Format	PDF
ISBN	9781467303125
e-ISBN	9781467303118
DOI	10.1109/INDIN.2012.6301136
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-07-25
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Context Shape Clustering algorithms Extraterrestrial measurements Complexity theory Computational efficiency
Content Type	Text
Resource Type	Article

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