### Table of Contents

# LIMBO Algorithm

# History

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:

- Creation of the Summary Artefacts
- Application of the AIB algorithm
- Associating original artefacts with clusters
- Determining the number of clusters