Handbook of Research on Cluster Theory

Handbook of Research on Cluster Theory

Handbooks of Research on Clusters series

Edited by Charlie Karlsson

Clusters have increasingly dominated local and regional development policies in recent decades and the growing intellectual and political interest for clusters and clustering is the prime motivation for this Handbook. Charlie Karlsson unites leading experts to present a thorough overview of economic cluster research.

Chapter 10: Cluster Using Wavelet Transformation

Abdullah Almasri and Ghazi Shukur

Subjects: economics and finance, regional economics, urban and regional studies, clusters, regional economics


10 Clustering using wavelet transformation Abdullah Almasri and Ghazi Shukur This chapter introduces and describes an alternative clustering approach based on the Discrete Wavelet Transform (DWT) which satisfies requirements that other clustering methods, like discriminative-based clustering and model-based clustering approaches, do not satisfy. The clustering method has been constructed using wavelet analysis that has the ability of decomposing a data set into different scales. Wavelet algorithm is then used to specify the number of the clusters and quality of the clustering results at each scale. The same algorithm can be generalized for more than one-dimensional data. Some examples about how to use this approach are presented in this chapter, using different sample sizes, and where different kinds of noises are imposed on simulated data. These examples show the successfulness and efficiency of this kind of methodology in detecting clusters under different situations. 1 Introduction Cluster analysis (originally used by Tryon, 1939) combines a number of different classification algorithms that are usually done to join cases or a set of data objects into groups or clusters when the group membership is not known a priori. Hence, it is a technique for linking individuals or objects into unknown groups or clusters such that those within each group or cluster are more closely related to one another than those assigned to other clusters. An observation or object can be explained by a number of measurements or by its relation to other observations or objects. Clustering,...

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