- Title
- SMEs and the economic growth: A comparative study of clustering techniques in SMEs data analysis
- Creator
- Mardaneh, Karim
- Date
- 2012
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/63030
- Identifier
- vital:6448
- Identifier
- ISBN:9780981902845
- Abstract
- Regional economic planning of small-to-medium enterprises (SMEs) requires a thorough understanding of the industry structure and the size of business. The main body of the literature regarding SMEs is focused on formation and growth, as well as success and failure (Dejardin & Fritsch, 2010). Some studies have considered clustering regional areas based on functional specialisation but only a few studies have considered industry structure and the size of business (Okamuro, 2006). This area of study may require large data sets and sophisticated clustering techniques, which have not been used in SMEs research. Using the Australian data and a large data set for regional (non-metropolitan) areas, this current study attempts to investigate the relationship between the economic growth of geographical areas with the industry structure and size of the businesses within those areas. For this the study uses Ward’s, the k-means, global k-means, and the modified global k-means clustering algorithms to cluster the Statistical Local Areas (SLA), and compares the function of these algorithms to identify the algorithm that performs the clustering task of the SMEs data more efficiently. Resulting analysis of this comparative study demonstrates that the modified global k-means algorithm outperforms the other algorithms examined.; E1
- Publisher
- Wellington, New Zealand
- Relation
- Conference Proceedings: 57th ICSB World Conference
- Rights
- Unknown copyright
- Rights
- This metadata is freely available under a CCO license
- Subject
- Firm size; Business location; Regional and metropolitan; Industry structure; Clustering
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