Algorithms for clustering data
Algorithms for clustering data
C4.5: programs for machine learning
C4.5: programs for machine learning
Multilevel k-way partitioning scheme for irregular graphs
Journal of Parallel and Distributed Computing
ACM Computing Surveys (CSUR)
Algorithms + Data Structures = Programs
Algorithms + Data Structures = Programs
Introduction to Algorithms
An Effective Multilevel Algorithm for Bisecting Graphs and Hypergraphs
IEEE Transactions on Computers
Finding optimal solutions to the graph partitioning problem with heuristic search
Annals of Mathematics and Artificial Intelligence
International Journal of Geographical Information Science
An alternative map of the United States based on an n-dimensional model of geographic space
Journal of Visual Languages and Computing
Exploratory hierarchical clustering for management zone delineation in precision agriculture
ICDM'11 Proceedings of the 11th international conference on Advances in data mining: applications and theoretical aspects
An AZP-ACO method for region-building
SETN'12 Proceedings of the 7th Hellenic conference on Artificial Intelligence: theories and applications
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Regionalization is to divide a large set of spatial objects into a number of spatially contiguous regions while optimizing an objective function, which is normally a homogeneity (or heterogeneity) measure of the derived regions. This research proposes and evaluates a family of six hierarchical regionalization methods. The six methods are based on three agglomerative clustering approaches, including the single linkage, average linkage (ALK), and the complete linkage (CLK), each of which is constrained with spatial contiguity in two different ways (i.e. the first-order constraining and the full-order constraining). It is discovered that both the Full-Order-CLK and the Full-Order-ALK methods significantly outperform existing methods across four quality evaluations: the total heterogeneity, region size balance, internal variation, and the preservation of data distribution. Moreover, the proposed algorithms are efficient and can find the solution in O(n 2log n) time. With such data scalability, for the first time it is possible to effectively regionalize large data sets that have 10 000 or more spatial objects. A detailed comparison and evaluation of the six methods are carried out with the 2004 US presidential election data.