Soil clustering by fuzzy c-means algorithm

Göktepe, A. Burak; Altun, Selim; Sezer, Alper

doi:10.1016/j.advengsoft.2005.01.008

Cited by 61 publications

(28 citation statements)

References 15 publications

(21 reference statements)

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“…In recent years, the fuzzy mathematics have extensively been used to consider the non-probabilistic uncertainties in various fields, including soil sciences and engineering (Burrough et al 1992;Metternicht 1997;Mitra et al 1998;Nisar Ahamed et al 2000;Kumar et al 2000;Zhu et al 2001;Tran et al 2002;Tayfur et al 2003;Bragato 2004;Goktepe et al 2005;Chen et al 2005;Bahrami et al 2005;Shekari and Baghernejad 2006;Lee and Lee 2006;Akyurek and Okalp 2007;Ferraro 2009). …”

Section: Introductionmentioning

confidence: 98%

Fuzzy pattern recognition method for assessing soil erosion

Saadatpour

Afshar

2010

Environ Monit Assess

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In this paper a PSIAC-based multi-parameter fuzzy pattern recognition (MPFPR) model is proposed and applied for classifying and ranking the potential soil erosion (PSE). In this approach, standard value matrix is used to define the membership degrees of each catchment to each class and the feature values are used for alternative ranking. The characteristic of PSE for each class is expressed by linguistic variables. The proposed method is straightforward, easy to understand, very practical, and its results may easily be interpreted. To assess the performance of the model, the results of PSIAC MPFPR and original PSIAC method are interpreted and compared with the observed data. It is shown that the proposed approach reflects the fuzzy nature of the soil erosion more efficiently and is quite robust for application in real world cases.

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Section: Introductionmentioning

confidence: 98%

Fuzzy pattern recognition method for assessing soil erosion

Saadatpour

Afshar

2010

Environ Monit Assess

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“…This method can be important supportive tool for the medical experts in diagnostic. Goktepe et al [7] proposed fuzzy c-means approach for soil clustering. They have found that fuzzy c-means exhibited better performance than k-means algorithm.…”

Section: Review Of Literaturementioning

confidence: 99%

A New Hybrid Hard-Fuzzy (K-Mfcm) Data Clustering Method for Finding Cluster Centroid

Jafar¹

2018

ijarcs

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Data mining is a collection of methods used to extract useful information from large data bases. Cluster Analysis refers to the grouping of a set of data points into clusters. Most widely used partitioning methods are K-means and Fuzzy c-means (FCM) algorithms. However, they suffer from the difficulties such as random selection of initial centre values and handling outlier data points. Most of the existing clustering methods use the Euclidean distance metric. The modified fuzzy c-means algorithm (MFCM) is efficient in handling outlier data points. In this paper, a new hybrid algorithm is proposed to solve the limitations of the traditional clustering methods. The hybrid K-MFCM algorithm is tested on four real world bench mark data sets from UCI machine learning repository with various distance metrics including Euclidean, City Block and Chessboard. The cluster centroid values of hybrid algorithm are calculated for various data sets. The experimental results show that the hybrid algorithm gives good results in terms of objective function value and better fuzzy cluster validity results for chessboard distance metric than other distance metrics.

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“…Clustering can be considered the most important unsupervised learning problem grouping sets of objects into clusters of similar objects. Applying these techniques on geotechnical data could be useful in identifying different lithology or formations with specific relative values and frequencies or distributions of certain attributes (Goktepe et al, 2005). In this application, K-Means algorithm (MacQueen, 1967) was utilized, which is considered as one of the simplest unsupervised learning algorithms that solve the well known clustering problem.…”

Section: Statistical and Data Mining Managermentioning

confidence: 99%