MCLEAN

MCLEAN transforms relational data into multilevel graph representations to detect and explore clusters within heterogeneous datasets.

Key Features:

• Graph-based transformation: Converts relational data into a graph format to represent relationships among elements.
• Multilevel representations: Produces hierarchical overview and detailed levels to show how elements merge into clusters.
• Community-finding algorithms: Employs community detection methods to identify cluster structure within the graph representation.
• Dynamic spatialization: Supports spatial layout of graph nodes across levels to reveal structural relationships.
• Heuristic results: Computes and presents heuristic outputs as entry points for further cluster exploration.
• Scalable analysis: Uses graph-based modeling and multilevel abstraction to address large or heterogeneous datasets.

Scientific Applications:

• Cluster detection in heterogeneous datasets: Identifies clusters in datasets with diverse or complex relationships.
• Exploratory pattern recognition: Reveals variable patterns and merging behaviors that may obscure underlying trends.
• Comparative clustering analysis: Provides an alternative representation to dendrograms for evaluating cluster structure.

Methodology:

MCLEAN transforms relational data into graph-based representations, applies community-finding algorithms to generate multilevel representations, and uses dynamic spatialization with heuristic scoring to present cluster structure.