Title :
Clustering by Mode Estimation
Author :
Ocelíková, E. ; Klimesova, D.
Author_Institution :
Dept. of Cybern. & Artificial Intell., Tech. Univ. of Kosice, Kosice
Abstract :
In this contribution we introduce a clustering scheme based on mode boundary detection procedures. Modes are characterized as compact regions of the data space with higher densities than their surrounding. A mode boundary as defined in this approach is an area of large local changes in the probability density functions. Examples of the performance of the clustering based on the so-obtained mode boundaries are given using artificially generated data sets.
Keywords :
estimation theory; probability; statistical analysis; clustering; mode boundary detection; mode estimation; probability density functions; Artificial intelligence; Cybernetics; Density functional theory; Hypercubes; Lattices; Multidimensional systems; Probability density function; Sampling methods; Statistics; Testing;
Conference_Titel :
Applied Machine Intelligence and Informatics, 2009. SAMI 2009. 7th International Symposium on
Conference_Location :
Herl´any
Print_ISBN :
978-1-4244-3801-3
Electronic_ISBN :
978-1-4244-3802-0
DOI :
10.1109/SAMI.2009.4956621
