Comparison of classification and clustering methods in spatial rainfall pattern recognition at Northern Iran

2010 ◽  
Vol 102 (3-4) ◽  
pp. 319-329 ◽  
Author(s):  
Saeed Golian ◽  
Bahram Saghafian ◽  
Sara Sheshangosht ◽  
Hossein Ghalkhani
Keyword(s):  
Pattern Recognition ◽  
Rainfall Pattern ◽  
Clustering Methods ◽  
Northern Iran ◽  
Classification And Clustering

Pattern recognition of major asperities using local recurrence time in Alborz Mountains, Northern Iran

2010 ◽  
Vol 14 (4) ◽  
pp. 787-802 ◽  
Author(s):  
Khalil Motaghi ◽  
Khaled Hessami ◽  
Mohammad Tatar
Keyword(s):  
Pattern Recognition ◽  
Local Recurrence ◽  
Recurrence Time ◽  
Northern Iran

scholarly journals THEORETICAL ANALYSIS OF PICTURE FUZZY CLUSTERING: CONVERGENCE AND PROPERTY

2018 ◽  
Vol 34 (1) ◽  
pp. 17-32 ◽  
Author(s):  
Pham Thi Minh Phuong ◽  
Pham Huy Thong ◽  
Le Hoang Son
Keyword(s):  
Pattern Recognition ◽  
Theoretical Analysis ◽  
Fuzzy Clustering ◽  
Computational Intelligence ◽  
Local Minimum ◽  
Clustering Algorithm ◽  
Clustering Methods ◽  
Mathematical Properties ◽  
Fuzzy Clustering Algorithm ◽  
Similarities And Differences

Recently, picture fuzzy clustering (FC-PFS) has been introduced as a new computational intelligence tool for various problems in knowledge discovery and pattern recognition. However, an important question that was lacked in the related researches is examination of mathematical properties behind the picture fuzzy clustering algorithm such as the convergence, the boundary or the convergence rate, etc. In this paper, we will prove that FC-PFS converges to at least one local minimum. The similarities and differences between this algorithm and other clustering methods are compared. Analysis on the loss function is also considered.

scholarly journals Application of unsupervised pattern recognition approaches for exploration of rare earth elements in Se-Chahun iron ore, central Iran

2017 ◽  
Vol 6 (2) ◽  
pp. 537-546 ◽  
Author(s):  
Mohammadali Sarparandeh ◽  
Ardeshir Hezarkhani
Keyword(s):  
Pattern Recognition ◽  
Rare Earth ◽  
Rare Earth Elements ◽  
Iron Ore ◽  
Experimental Studies ◽  
Geochemical Data ◽  
Clustering Methods ◽  
Agglomerative Clustering ◽  
Hidden Information ◽  
Icp Ms

Abstract. The use of efficient methods for data processing has always been of interest to researchers in the field of earth sciences. Pattern recognition techniques are appropriate methods for high-dimensional data such as geochemical data. Evaluation of the geochemical distribution of rare earth elements (REEs) requires the use of such methods. In particular, the multivariate nature of REE data makes them a good target for numerical analysis. The main subject of this paper is application of unsupervised pattern recognition approaches in evaluating geochemical distribution of REEs in the Kiruna type magnetite–apatite deposit of Se-Chahun. For this purpose, 42 bulk lithology samples were collected from the Se-Chahun iron ore deposit. In this study, 14 rare earth elements were measured with inductively coupled plasma mass spectrometry (ICP-MS). Pattern recognition makes it possible to evaluate the relations between the samples based on all these 14 features, simultaneously. In addition to providing easy solutions, discovery of the hidden information and relations of data samples is the advantage of these methods. Therefore, four clustering methods (unsupervised pattern recognition) – including a modified basic sequential algorithmic scheme (MBSAS), hierarchical (agglomerative) clustering, k-means clustering and self-organizing map (SOM) – were applied and results were evaluated using the silhouette criterion. Samples were clustered in four types. Finally, the results of this study were validated with geological facts and analysis results from, for example, scanning electron microscopy (SEM), X-ray diffraction (XRD), ICP-MS and optical mineralogy. The results of the k-means clustering and SOM methods have the best matches with reality, with experimental studies of samples and with field surveys. Since only the rare earth elements are used in this division, a good agreement of the results with lithology is considerable. It is concluded that the combination of the proposed methods and geological studies leads to finding some hidden information, and this approach has the best results compared to using only one of them.

scholarly journals A Systematic Comparative Analysis of Clustering Techniques

2020 ◽  
Vol 25 (2) ◽  
pp. 87-104
Author(s):  
Satinder Bal Gupta ◽  
Rajkumar Yadav ◽  
Shivani Gupta
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Information Retrieval ◽  
Comparative Analysis ◽  
Time Complexity ◽  
Clustering Methods ◽  
Clustering Techniques ◽  
Day By Day

AbstractClustering has now become a very important tool to manage the data in many areas such as pattern recognition, machine learning, information retrieval etc. The database is increasing day by day and thus it is required to maintain the data in such a manner that useful information can easily be extracted and used accordingly. In this process, clustering plays an important role as it forms clusters of the data on the basis of similarity in data. There are more than hundred clustering methods and algorithms that can be used for mining the data but all these algorithms do not provide models for their clusters and thus it becomes difficult to categorise all of them. This paper describes the most commonly used and popular clustering techniques and also compares them on the basis of their merits, demerits and time complexity.

Number Recognition in the Saudi License Plates using Classification and Clustering Methods

2017 ◽  
Vol 11 (1) ◽  
pp. 123-135 ◽  
Author(s):  
Salah Al-Shami ◽  
Ali El-Zaart ◽  
Ahmed Zekri ◽  
Khaled Almustafa ◽  
Rached Zantout
Keyword(s):  
Clustering Methods ◽  
Number Recognition ◽  
Classification And Clustering

scholarly journals Classification and Clustering Methods for Multiple Environmental Factors in Gene–Environment Interaction

Epidemiology ◽  
2016 ◽  
Vol 27 (6) ◽  
pp. 870-878 ◽  
Author(s):  
Yi-An Ko ◽  
Bhramar Mukherjee ◽  
Jennifer A. Smith ◽  
Sharon L. R. Kardia ◽  
Matthew Allison ◽  
...  
Keyword(s):  
Environmental Factors ◽  
Environment Interaction ◽  
Clustering Methods ◽  
Gene Environment Interaction ◽  
Gene Environment ◽  
Classification And Clustering
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