Clustering Analysis Methodology for employment and Regional Planning in Greece

Michaelides, Panayotis G. and Economakis, George and Lagos, Dimitrios (2006): Clustering Analysis Methodology for employment and Regional Planning in Greece.

Abstract

he Clustering Analysis Methodology is used for employment and Regional Planning in Greece. The thirteen regional departments of Greece are assembled into clusters presenting similar employment characteristics. The results of this study confirm those obtained by a previous study using an alternative methodology.

• Item Type: MPRA Paper
• Language: English
• Keywords: Clustering Analysis Methodology, Regional Planning, Employment characteristics, Greek Regions, Sustainable Development
• Subjects: R - Urban, Rural, Regional, Real Estate, and Transportation Economics > R1 - General Regional Economics; R - Urban, Rural, Regional, Real Estate, and Transportation Economics > R1 - General Regional Economics > R11 - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes

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Web-link of the CSNA provides a review of On-Line Software for Clustering and Multivariate Analysis at web location: http://www.pitt.edu/~csna/software.html ; it Includes Statlib (statistical software of all sorts), CMLIB (Core Mathematics Library from NIST. CLUSTER - It includes routines for clustering variables and/or observations using algorithms such as direct joining and splitting, Fisher's exact optimization, single- link, K-means, and minimum mutations, and routines for estimating missing values.), APSTAT (Selected Algorithms Transcribed from Applied Statistics. Includes implementations of: minimal spanning tree, single-link hierarchical clustering, discriminant analysis of categorical data, branch and bound algorithm for feature subset selection, etc.), GENERAL (Software of General Statistical Interest. Includes the 3-d interactive data display package, XGobi. Algorithms for convex hull, and Delaunay triangulation. Mclust, model-based clustering routines (Banfield and Raftery). MVE, minimum volume ellipsoid estimator (Rousseeuw), PROGRESS, robust regression (Rousseeuw and Leroy), MARS, projection pursuit. Nonlinear discriminant analysis. LOESS regression. Etc.), MULTI (Multivariate Analysis and Clustering. Hierarchical clustering, principal components analysis, discriminant analysis. Programs for multivariate data analysis and graphical display, linear regression with errors in both variables, software directory including details of packages for phylogeny estimation and to support consensus clustering.), NETLIB (at AT&T Bell Laboratories is a major site for numerical analysis software, including eigenvalue/vector packages EISPACK, SVDPACK, etc.), CLUSTER (cluster utility. ... performs Hierarchical Cluster Analysis (HCA) on a set of vectors and outputs the result in a variety of formats on standard output. ... performs Principal Component Analysis (PCA) on a set of vectors and prints the transformed set of vectors on standard output.), QUICKHULL (The algorithm calculates the Delaunay triangulation and convex hull.), DELTREE (Olivier Devillers provides deltree.c which computes the V oronoi/Delaunay diagrams and also has a function that returns the nearest neighbour pt. in the diagram to any arbitarily chosen point. (Comments in French)).

Web-link of Statsoft at Web location: http://www.statsoft.com/textbook/stcluan.ht ml presents an overview of all the important clustering techniques such as: Joining – Tree Clustering (Hierarchical Tree, Distance Measures - Euclidean distance; Squared Euclidean distance; City-block (Manhattan) distance; Chebychev distance; Power distance; Percent disagreement ), Amalgamation or Linkage Rules (Single linkage -nearest neighbor, Complete linkage -furthest neighbor, Unweighted pair-group average, Weighted pair-group average, Unweighted pair-group centroid, Weighted pair-group centroid –median, Ward's method), Two-way Joining, k-Means Clustering, Expectation Maximization Clustering. [63] Web-link for Fuzzy Cluster Analysis at Web link: http://www.fuzzy-clustering.de/ Developed under Linux, The fuzzy clustering (fc) package contains well-known algorithms like the fuzzy c-means algorithm and the algorithm by Gustafson and Kessel, but also more recent developments.