Fuzzy c-means cluster analysis of early diagenetic effects on natural remanent magnetisation acquisition in a 1.1 Myr piston core from the Central Mediterranean

1994 ◽  
Vol 85 (1-2) ◽  
pp. 155-171 ◽  
Author(s):  
M.J. Dekkers ◽  
C.G. Langereis ◽  
S.P. Vriend ◽  
P.J.M. van Santvoort ◽  
G.J. de Lange
Keyword(s):  
Cluster Analysis ◽  
Central Mediterranean ◽  
Piston Core ◽  
Fuzzy C Means ◽  
Remanent Magnetisation ◽  
Natural Remanent Magnetisation

Cluster Analysis with Various Algorithms for Mixed Data

2017 ◽  
pp. 282-317
Author(s):  
Abha Sharma ◽  
R. S. Thakur
Keyword(s):  
Cluster Analysis ◽  
Clustering Algorithms ◽  
Complex Problem ◽  
Experimental Results ◽  
Mixed Data ◽  
Data Set ◽  
Fuzzy C Means ◽  
Conversion Method ◽  
Inherent Structure ◽  
Numeric Data

Analyzing clustering of mixed data set is a complex problem. Very useful clustering algorithms like k-means, fuzzy c-means, hierarchical methods etc. developed to extract hidden groups from numeric data. In this paper, the mixed data is converted into pure numeric with a conversion method, the various algorithm of numeric data has been applied on various well known mixed datasets, to exploit the inherent structure of the mixed data. Experimental results shows how smoothly the mixed data is giving better results on universally applicable clustering algorithms for numeric data.

scholarly journals Soft clustering using real-world data for the identification of multimorbidity patterns in an elderly population: cross-sectional study in a Mediterranean population

BMJ Open ◽  
2019 ◽  
Vol 9 (8) ◽  
pp. e029594 ◽  
Author(s):  
Concepción Violán ◽  
Quintí Foguet-Boreu ◽  
Sergio Fernández-Bertolín ◽  
Marina Guisado-Clavero ◽  
Margarita Cabrera-Bean ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Electronic Health Records ◽  
Cross Sectional Study ◽  
Secondary Outcome ◽  
Sectional Study ◽  
Cross Sectional ◽  
Health Records ◽  
Soft Clustering ◽  
Fuzzy C Means ◽  
Electronic Health

ObjectivesThe aim of this study was to identify, with soft clustering methods, multimorbidity patterns in the electronic health records of a population ≥65 years, and to analyse such patterns in accordance with the different prevalence cut-off points applied. Fuzzy cluster analysis allows individuals to be linked simultaneously to multiple clusters and is more consistent with clinical experience than other approaches frequently found in the literature.DesignA cross-sectional study was conducted based on data from electronic health records.Setting284 primary healthcare centres in Catalonia, Spain (2012).Participants916 619 eligible individuals were included (women: 57.7%).Primary and secondary outcome measuresWe extracted data on demographics, International Classification of Diseases version 10 chronic diagnoses, prescribed drugs and socioeconomic status for patients aged ≥65. Following principal component analysis of categorical and continuous variables for dimensionality reduction, machine learning techniques were applied for the identification of disease clusters in a fuzzy c-means analysis. Sensitivity analyses, with different prevalence cut-off points for chronic diseases, were also conducted. Solutions were evaluated from clinical consistency and significance criteria.ResultsMultimorbidity was present in 93.1%. Eight clusters were identified with a varying number of disease values: nervous and digestive; respiratory, circulatory and nervous; circulatory and digestive; mental, nervous and digestive, female dominant; mental, digestive and blood, female oldest-old dominant; nervous, musculoskeletal and circulatory, female dominant; genitourinary, mental and musculoskeletal, male dominant; and non-specified, youngest-old dominant. Nuclear diseases were identified for each cluster independently of the prevalence cut-off point considered.ConclusionsMultimorbidity patterns were obtained using fuzzy c-means cluster analysis. They are clinically meaningful clusters which support the development of tailored approaches to multimorbidity management and further research.

The application of fuzzy c-means cluster analysis and non-linear mapping to geochemical datasets: examples from Portugal

1988 ◽  
Vol 3 (2) ◽  
pp. 213-224 ◽  
Author(s):  
S.P. Vriend ◽  
P.F.M. van Gaans ◽  
J. Middelburg ◽  
A. de Nijs
Keyword(s):  
Cluster Analysis ◽  
Linear Mapping ◽  
Fuzzy C Means ◽  
Non Linear

scholarly journals Fuzzy clustering and fuzzy c-means partition cluster analysis and validation studies on a subset of citescore dataset

2019 ◽  
Vol 9 (4) ◽  
pp. 2760
Author(s):  
K. Varada Rajkumar ◽  
Adimulam Yesubabu ◽  
K. Subrahmanyam
Keyword(s):  
Cluster Analysis ◽  
Fuzzy Clustering ◽  
Time Complexity ◽  
Clustering Algorithm ◽  
Fuzzy Cluster ◽  
Fuzzy Cluster Analysis ◽  
Fuzzy C Means ◽  
A Value ◽  
Data Points ◽  
Partition Clustering

A hard partition clustering algorithm assigns equally distant points to one of the clusters, where each datum has the probability to appear in simultaneous assignment to further clusters. The fuzzy cluster analysis assigns membership coefficients of data points which are equidistant between two clusters so the information directs have a place toward in excess of one cluster in the meantime. For a subset of CiteScore dataset, fuzzy clustering (fanny) and fuzzy c-means (fcm) algorithms were implemented to study the data points that lie equally distant from each other. Before analysis, clusterability of the dataset was evaluated with Hopkins statistic which resulted in 0.4371, a value &lt; 0.5, indicating that the data is highly clusterable. The optimal clusters were determined using NbClust package, where it is evidenced that 9 various indices proposed 3 cluster solutions as best clusters. Further, appropriate value of fuzziness parameter <em>m</em> was evaluated to determine the distribution of membership values with variation in <em>m</em> from 1 to 2. Coefficient of variation (CV), also known as relative variability was evaluated to study the spread of data. The time complexity of fuzzy clustering (fanny) and fuzzy c-means algorithms were evaluated by keeping data points constant and varying number of clusters.

scholarly journals Fuzzy C-Means Partition Cluster Analysis and Validation Studies on a Subset of CiteScore Dataset

2019 ◽  
Vol 8 (9S3) ◽  
pp. 105-107
Keyword(s):  
Cluster Analysis ◽  
Laser Welding ◽  
Validation Studies ◽  
Welded Joint ◽  
Experimental Studies ◽  
Pulsed Laser ◽  
Welding Process ◽  
Pulsed Laser Welding ◽  
Fuzzy C Means

The article deals with promising areas of application of pulsed laser welding for products made of silver-based alloys. The results of experimental studies to improve the quality of the welded joint and the efficiency of the welding process with the use of activated absorption additives are presented

scholarly journals Cluster Analysis Menggunakan Algoritma Fuzzy C-means dan K-means Untuk Klasterisasi dan Pemetaan Lahan Pertanian di Minahasa Tenggara

2017 ◽  
Vol 8 (3) ◽  
Author(s):  
Jemaictry Tamaela ◽  
Eko Sediyono ◽  
Adi Setiawan
Keyword(s):  
Data Mining ◽  
Cluster Analysis ◽  
Open Source ◽  
Visual Information ◽  
Agricultural Land ◽  
Agricultural Lands ◽  
Fuzzy C Means ◽  
Gis Data ◽  
Perform Cluster Analysis

Abstract. The purpose of this study is to perform cluster analysis and implementation by utilizing fuzzyc-means (FCM) and k-means (KM) to process agricultural data based on the data mining results. The fuzzy c-means (FCM) and k-means (KM) are implemented to find out and form the agricultural land clusters which appropriate the commodity types based on the supporting attributes that are used. The analysis and implementation results could provide some land information such as the number of the clusters, the land areas, the region areas, the locations and the productivity levels. The results of this study could be applied as the suggestion in converting the land functions and structuring the agricultural lands. The utilization of Openstreetmap is an open source solution which is implemented in the application. It could give visual information related to the agricultural land regions based on the clusters which make it easier to comprehend. Keywords: Cluster analysis, C-means, K-means, GIS, Data mining Abstrak. Penelitian ini bertujuan untuk melakukan analisis cluster dan implementasinya dengan menggunakan algoritma fuzzy c-means (FCM) dan k-means (KM) untuk mengelola data  pertanian dari hasil data mining yang dilakukan. Fuzzy c-means (FCM) dan k-means (KM) dimplementasikan untuk menemukan dan membentuk klaster-klaster daerah lahan pertanian sesuai dengan jenis komoditi berdasarkan atribut-atribut pendukung yang digunakan. Hasil analisis dan implementasi dapat menyediakan informasi lahan seperti jumlah kluster, luas lahan, luas daerah, letak dan tingkat produktifitas. Hasil yang diperoleh dapat menjadi bahan masukan dalam proses alih fungsi dan penataan lahan pertanian. Penggunaan Openstreetmap merupakan solusi open source yang diimplementasikan pada aplikasi dapat memberikan informasi  visual daerah-daerah lahan pertanian berdasarkan klaster yang dihasilkan sehingga lebih mudah untuk dipahami.Keywords: Cluster analysis, c-means, k-means, GIS, Data mining

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