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LIMBO Algorithm


The algorithm was developed by Periklis Andritsos and Vassilios Tzerpos.

Algorithm Intent

To generate decompositions that exhibit the least information lost when entities are represented by their clusters.

Factbase Properties

The factbase is a generic dependency data table where each row describes one entity to be clustered. Each column contains the value for a specific attribute.

Clustering Objectives

The Limbo algorithm produces software decompositions with minimum information loss.

Process Description

An agglomerative clustering algorithm that on each step merges the two clusters with the least information loss.

The information lost is calculated using the Agglomerative Information Bottleneck algorithm.

Decomposition Properties

The LIMBO decomposition has a small value for its information lost function.

Algorithm Restrictions

Failed Assumptions

Detailed Algorithm Description

LIMBO has four phases:

  1. Creation of the Summary Artefacts
  2. Application of the AIB algorithm
  3. Associating original artefacts with clusters
  4. Determining the number of clusters
Last modified:
2010/05/06 10:12