LC: A conceptual clustering algorithm Conference Paper

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Overview

abstract

• An algorithm for automated construction of conceptual classification is presented. The algorithm arranges the objects into clusters using clustering criterion based on graph theory and constructs the concepts based on attributes that distinguish objects in the different computed clusters (typical testors). The algorithm may be applied in problems where the objects can be described simultaneously by qualitative and quantitative attributes (mixed data); with incomplete descriptions (missing data) and the number of clusters to be formed is not known a priori. LC has been tested on data from standard public databases. © Springer-Verlag Berlin Heidelberg 2001.

authors

• Martínez-Trinidad J.F.
• Sánchez Díaz Guillermo

publication date

• 2001-01-01

published in

• Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)  Journal

Research

keywords

• Data mining; Graph theory; Machine learning; Pattern recognition; Algorithms; Artificial intelligence; Learning systems; Automated construction; Clustering criteria; Conceptual clustering; Missing data; Mixed data; Number of clusters; Public database; Quantitative attributes; Clustering algorithms

Identity

Digital Object Identifier (DOI)

• 10.1007/3-540-44596-x_10

Additional Document Info

start page

• 117

end page

• 127

volume

• 2123 LNAI