Comparison of two fuzzy algorithms in geodemographic segmentation analysis: The Fuzzy C-Means and Gustafson–Kessel methods

Grekousis, George; Hatzichristos, Thomas

doi:10.1016/j.apgeog.2011.11.004

Cited by 52 publications

(31 citation statements)

References 22 publications

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“…Fuzzy clustering methods are often used in GDA because they assign a membership value for each area instead of assigning a geographical area to a single group, so that the issues of ecological fallacy can be solved [31]. Geo-demographic have been adopted with apparent success, and delineating the social and demographical profile of small areas [10]. Literature shows that there are some relevant works concerning the applications and algorithms for GDA such as in [8,[27][28][29][30][32][33][34][35].…”

Section: The Gda Principles and Relevant Workmentioning

confidence: 99%

“…It is expressed in numerous current literatures that GDA is generally characterized as the investigation of spatially referenced geo-demographic information, which investigates the individuals based on their residential status [10,20]. In order to make the geo-demographic data more meaningful and manageable, some clustering methods are utilized in GDA to classify those data into several clusters [31].…”

Section: The Gda Principles and Relevant Workmentioning

confidence: 99%

“…Clustering, a kind of data mining methods is the process to create segmentation from the entire data set into relatively homogeneous subgroups or clusters, in which the similarity of the instances within the cluster is maximized and the similarity to instances outside the cluster is minimized [11,19,38]. This method is typically used to break down statistical information to get valuable data from substantial scale records [10].…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy geographically weighted clustering using artificial bee colony: An efficient geo-demographic analysis algorithm and applications to the analysis of crime behavior in population

2015

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Geo-demographic analysis is an essential part of a geographical information system (GIS) for predicting people's behavior based on statistical models and their residential location. Fuzzy Geographically Weighted Clustering (FGWC) serves as one of the most efficient algorithms in geo-demographic analysis. Despite being an effective algorithm, FGWC is sensitive to initialize when the random selection of cluster centers makes the iterative process falling into the local optimal solution easily. Artificial Bee Colony (ABC), one of the most popular meta-heuristic algorithms, can be regarded as the tool to achieve global optimization solutions. This research aims to propose a novel geo-demographic analysis algorithm that integrates FGWC to the optimization scheme of ABC for improving geo-demographic clustering accuracy. Experimental results on various datasets show that the clustering quality of the proposed algorithm called FGWC-ABC is better than those of other relevant methods. The proposed algorithm is also applied to a decision-making application for analyzing crime behavior problem in the population using the US communities and crime dataset. It provides fuzzy rules to determine the violent crime rate in terms of linguistic labels from socioeconomic variables. These results are significant to make predictions of further US violent crime rate and to facilitate appropriate decisions on prevention such the situations in the future.

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Section: The Gda Principles and Relevant Workmentioning

confidence: 99%

Section: The Gda Principles and Relevant Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fuzzy geographically weighted clustering using artificial bee colony: An efficient geo-demographic analysis algorithm and applications to the analysis of crime behavior in population

2015

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“…In the binary logic or common clustering, the set is limited by the binary yes/no definition, meaning that each object corresponds to one cluster only. But in fuzzy clustering, objects can be associated with multiple clusters to different degrees (Grekousis and Thomas, 2012;Grekousis et al 2013). Each cluster has a cluster center that represents a typical object in the cluster and a membership value (between 0 and 1) that reveals how close each object is to the center of a cluster.…”

Section: Child-woman Ratiomentioning

confidence: 99%

A Divided Capital: Residential Segregation In Ankara

Ataç

2016

METU JFA

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“…The CE index measures the fuzziness degree of a cluster partition, defined in Eq. (14) as follows [23,24].…”

Section: Classification Entropy (Ce) Validity Indexmentioning

confidence: 99%

Improvement of Fuzzy Geographically Weighted Clustering-Ant Colony Optimization Performance using Context-Based Clustering and CUDA Parallel Programming

Nurmala

Purwarianti

2017

J. ICT Res. Appl.

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Abstract. Geo-demographic analysis (GDA) is the study of population characteristics by geographical area. Fuzzy Geographically Weighted Clustering (FGWC) is an effective algorithm used in GDA. Improvement of FGWC has been done by integrating a metaheuristic algorithm, Ant Colony Optimization (ACO), as a global optimization tool to increase the clustering accuracy in the initial stage of the FGWC algorithm. However, using ACO in FGWC increases the time to run the algorithm compared to the standard FGWC algorithm. In this paper, context-based clustering and CUDA parallel programming are proposed to improve the performance of the improved algorithm (FGWC-ACO). Contextbased clustering is a method that focuses on the grouping of data based on certain conditions, while CUDA parallel programming is a method that uses the graphical processing unit (GPU) as a parallel processing tool. The Indonesian Population Census 2010 was used as the experimental dataset. It was shown that the proposed methods were able to improve the performance of FGWC-ACO without reducing the clustering quality of the original method. The clustering quality was evaluated using the clustering validity index.

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Comparison of two fuzzy algorithms in geodemographic segmentation analysis: The Fuzzy C-Means and Gustafson–Kessel methods

Cited by 52 publications

References 22 publications

Fuzzy geographically weighted clustering using artificial bee colony: An efficient geo-demographic analysis algorithm and applications to the analysis of crime behavior in population

Fuzzy geographically weighted clustering using artificial bee colony: An efficient geo-demographic analysis algorithm and applications to the analysis of crime behavior in population

A Divided Capital: Residential Segregation In Ankara

Improvement of Fuzzy Geographically Weighted Clustering-Ant Colony Optimization Performance using Context-Based Clustering and CUDA Parallel Programming

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