• Summary
  • Contents
  • Subject index

A must-have reference resource for qualitative management researchers, this dictionary contains over 90 entries covering the fundamentals of qualitative methodologies; covering both analysis and implementation. Each entry gives an introduction to the topic, lists the key relevant features, gives a worked example, a concise summary and a selection of further reading suggestions. It is suitable for researchers and academics who need a handy and quick point of reference.

Clustering Algorithms
Clustering algorithms
Introduction

Clustering analysis is a multivariate analysis technique that seeks to organize objects described by a number of attributes or variables into relatively homogeneous groups, or ‘clusters’. An optimal solution of clustering problems can be obtained by comparing the performance criterion of all combinations of objects. For this reason, problems with small numbers of objects can be solved exactly. For the large problem, however, such exhaustive enumeration approaches would be impractical since the number of combinations grows exponentially with the number of objects. Because of the ill-defined nature of cluster analysis problems, many different types of cluster analysis algorithms have been proposed. Anderberg (1973) separated clustering methods into two types: hierarchical and non-hierarchical clustering methods. We will review these two types of methods in ...

  • Loading...
locked icon

Sign in to access this content

Get a 30 day FREE TRIAL

  • Watch videos from a variety of sources bringing classroom topics to life
  • Read modern, diverse business cases
  • Explore hundreds of books and reference titles