One of the fundamental tasks of unsupervised learning is dataset clustering, to partition the input dataset into clusters composed by somehow “similar” objects that “differ” from the objects belonging to other classes. To this end, in this paper we assume that the different clusters are drawn from different, possibly intersecting, geometrical structures represented by manifolds embedded into a possibly higher dimensional space. Under these assumptions, and considering that each manifold is typified by a geometrical structure characterized by its intrinsic dimensionality, which (possibly) differs from the intrinsic dimensionalities of other manifolds, we code the input data by means of local intrinsic dimensionality estimates and features related to them, and we subsequently apply simple and basic clustering algorithms, since our interest is specifically aimed at assessing the discriminative power of the proposed features. Indeed, their encouraging discriminative quality is shown by a feature relevance test, by the clustering results achieved on both synthetic and real datasets, and by their comparison to those obtained by related and classical state-of-the-art clustering approaches.
Local intrinsic dimensionality based features for clustering / P. Campadelli, E. Casiraghi, C. Ceruti, G. Lombardi, A. Rozza - In: Image analysis and processing – ICIAP 2013 : 17th international conference : Naples, Italy, september 9-13, 2013 : proceedings. Part 1 / [a cura di] A. Petrosino. - Berlin : Springer, 2013. - ISBN 9783642411809. - pp. 41-50 (( Intervento presentato al 17. convegno Image Analysis and Processing international conference (ICIAP) tenutosi a Napoli nel 2013.
|Titolo:||Local intrinsic dimensionality based features for clustering|
|Settore Scientifico Disciplinare:||Settore INF/01 - Informatica|
|Data di pubblicazione:||2013|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1007/978-3-642-41181-6_5|
|Tipologia:||Book Part (author)|
|Appare nelle tipologie:||03 - Contributo in volume|