A lossless DEM compression for fast retrieval method using fuzzy clustering and MANFIS neural network

Sơn, Lê Hoàng; Linh, Nguyen Duy; Long, Hoàng Việt

doi:10.1016/j.engappai.2013.12.002

Cited by 32 publications

(13 citation statements)

References 14 publications

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“…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]. Among all existing relevant works, Fuzzy Geographically Weighted Clustering (FGWC) [20] is considered one of the most efficient algorithms for the GDA problem.…”

Section: The Gda Principles and Relevant Workmentioning

confidence: 99%

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%

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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“…Some works proposed by Butkiewicz (2012) and Zhao et al (2013) developed fuzzy features and distance measures to assess the clustering quality. Son et al (2012aSon et al ( , b, 2013Son et al ( , 2014 and Son (2014aSon ( , b, c, 2015 proposed intuitionistic fuzzy clustering algorithms for geodemographic analysis based on recent results regarding IFS and the possibilistic FCM. Kernel-based fuzzy clustering (KFCM) was applied to enhance the clustering quality of FCM such as in Graves and Pedrycz (2010), Kaur et al (2012) and Lin (2014).…”

Section: Introductionmentioning

confidence: 98%

Picture fuzzy clustering: a new computational intelligence method

2015

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Fuzzy clustering especially fuzzy C-means (FCM) is considered as a useful tool in the processes of pattern recognition and knowledge discovery from a database; thus being applied to various crucial, socioeconomic applications. Nevertheless, the clustering quality of FCM is not high since this algorithm is deployed on the basis of the traditional fuzzy sets, which have some limitations in the membership representation, the determination of hesitancy and the vagueness of prototype parameters. Various improvement versions of FCM on some extensions of the traditional fuzzy sets have been proposed to tackle with those limitations. In this paper, we consider another improvement of FCM on the picture fuzzy sets, which is a generalization of the traditional fuzzy sets and the intuitionistic fuzzy sets, and present a novel picture fuzzy clustering algorithm, the so-called FC-PFS. A numerical example on the IRIS dataset is conducted to illustrate the activities of the proposed algorithm. The experimental results on various benchmark datasets of UCI Machine Learning Repository under different scenarios of parameters of the algorithm reveal that FC-PFS has better Communicated by V. Loia.

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“…Nonetheless, using hard clustering for GDA often leads to the issues of ecological fallacy, which can be shortly understood that statistics accurately describing group characteristics do not necessarily apply to individuals within that group. For this fact, Fuzzy C-Means (FCM) and its variants were considered as the appropriate methods to determine the distribution of a demographic feature on a map as described in some articles such as [1,5,10,[12][13][14][15][16][17][18][19]. Since the results of FCM are independent to the geographical factors, some improvements of that algorithm were made by attaching FCM with a spatial model such as SIM in [3] and SIM-PF in [7,16,18].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Approaches to Context Variables in Fuzzy Geographically Weighted Clustering

Minh¹,

Sơn²

2015

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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Fuzzy Geographically Weighted Clustering (FGWC) is considered as a suitable tool for the analysis of geo-demographic data that assists the provision and planning of products and services to local people. Context variables were attached to FGWC in order to accelerate the computing speed of the algorithm and to focus the results on the domain of interests. Nonetheless, the determination of exact, crisp values of the context variable is a hard task. In this paper, we propose two novel methods using fuzzy approaches for that determination. A numerical example is given to illustrate the uses of the proposed methods.

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A lossless DEM compression for fast retrieval method using fuzzy clustering and MANFIS neural network

Cited by 32 publications

References 14 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

Picture fuzzy clustering: a new computational intelligence method

Fuzzy Approaches to Context Variables in Fuzzy Geographically Weighted Clustering

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