Clustering hierarchical data using self-organizing map : A graph-theoretical approach

• The application of Self-Organizing Map (SOM) to hierarchical data remains an open issue, because such data lack inherent quantitative information. Past studies have suggested binary encoding and Generalizing SOM as techniques that transform hierarchical data into numerical attributes. Based on graph theory, this paper puts forward a novel approach that processes hierarchical data into a numerical representation for SOM-based clustering. The paper validates the proposed graph-theoretical approach via complexity theory and experiments on real-life data. The results suggest that the graph-theoretical approach has lower algorithmic complexity than Generalizing SOM, and can yield SOM having significantly higher cluster validity than binary encoding does. Thus, the graph-theoretical approach can form a data-preprocessing step that extends SOM to the domain of hierarchical data.

Subject headings

SAMHÄLLSVETENSKAP  -- Ekonomi och näringsliv -- Företagsekonomi (hsv//swe)

SOCIAL SCIENCES  -- Economics and Business -- Business Administration (hsv//eng)

NATURVETENSKAP  -- Data- och informationsvetenskap -- Systemvetenskap, informationssystem och informatik (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences -- Information Systems (hsv//eng)

Keyword

Clustering

Graph theory

Hierarchical data

SOM

Algorithmic complexity

Binary encodings

Cluster validity

Complexity theory

Graph theoretical approach

Numerical attributes

Numerical representation

Pre-processing step

Quantitative information

Real life data

Selforganizing map

Computational complexity

Conformal mapping

Electric converters

Encoding (symbols)

Parallel processing systems

Self organizing maps

Strength of materials

Publication and Content Type

ref (subject category)

kon (subject category)