Site Relationships at Quebrada Tarapaca, Chile: A Comparison of Clustering and Scaling Techniques

1974 ◽  
Vol 39 (1) ◽  
pp. 51-74 ◽  
Author(s):  
R. G. Matson ◽  
D. L. True
Keyword(s):  
Multidimensional Scaling ◽  
Frequency Analysis ◽  
Relative Frequency ◽  
Northern Chile ◽  
Clustering Methods ◽  
Comparison Of The Results

AbstractThis study is a comparison of the results of a variety of clustering methods and 2 multidimensional scaling techniques on data from sites in northern Chile. While differences do occur, the similarities among the results are strong in spite of differing inputs. In general, results of relative frequency analysis appear to be superior to those of presence/absence, and the techniques used seem to be viable additions to existing archaeological tools.

PERAN MASYARAKAT TERHADAP PEMBAYARAN RETRIBUSI SAMPAH DI KOTA SAMARINDA

2018 ◽  
Vol 2 (1) ◽  
pp. 33
Author(s):  
Abd Rachim AF,
Keyword(s):  
Waste Management ◽  
Population Growth ◽  
Frequency Analysis ◽  
Urban Areas ◽  
Relative Frequency ◽  
Environmental Problems ◽  
Living Standards ◽  
The Public ◽  
And Behavior

One of the environmental problems in urban areas is the pollution caused by garbage. The waste problem is caused by various factors such as population growth, living standards changes, lifestyles and behavior, as well as how the waste management system. This study aims to determine how the role of society to levy payments garbage in Samarinda. This research was descriptive; where the data is collected then compiled, described and analyzed used relative frequency analysis. The participation of the public to pay a "levy junk", which stated to pay 96.67%, for each month and the rates stated society cheap, moderate and fairly, respectively 46.08%, 21.21%, 21.04%. Base on the data , the role of the community to pay "levy junk" quite high.

scholarly journals Discretization of Fractional Operators: Analysis by Means of Advanced Computational Techniques

Mathematics ◽  
2021 ◽  
Vol 9 (19) ◽  
pp. 2429
Author(s):  
Jose Tenreiro Machado ◽  
Alexandra M. Galhano ◽  
Carla S. Cordeiro
Keyword(s):  
Multidimensional Scaling ◽  
Hierarchical Clustering ◽  
Computational Techniques ◽  
Graphical Representations ◽  
Fractional Operator ◽  
Fractional Operators ◽  
Clustering Methods ◽  
Generalized Mean ◽  
Cosine Distance ◽  
Fractional Orders

This paper studies the discretization of fractional operators by means of advanced clustering methods. The Grünwald–Letnikov fractional operator is approximated by series generated by the Euler, Tustin and generalized mean. The series for different fractional orders form the objects to be assessed. For this purpose, the several distances associated with the hierarchical clustering and multidimensional scaling computational techniques are tested. The Arc-cosine distance and the 3-dim multidimensional scaling produce good results. The visualization of the graphical representations allows a better understanding of the properties embedded in each type of approximation of the fractional operators.

scholarly journals Efecto del fuego en la estructura poblacional y la regeneración de dos especies de encinos (Quercus liebmanii Oerst. y Quercus magnoliifolia Née) en la región de La Montaña (Guerrero), México

2017 ◽  
pp. 5
Author(s):  
Víctor M. Peña-Ramírez ◽  
Consuelo Bonfíl
Keyword(s):  
Population Structure ◽  
Frequency Analysis ◽  
High Frequency ◽  
Relative Frequency ◽  
San Jose ◽  
Southern Mexico ◽  
Crown Area ◽  
Crown Cover ◽  
Adult Trees ◽  
Adult Tree

This study analyzed the effect of superficial fires on population structure and individual crown area of two tree species: Quercus liebmanii and Q. magnoliifolia, in San José Lagunas, Guerrero, southern Mexico. For each species two sites were selected, in which ca. 350 individuals were recorded per species. Individuals were classified in four size categories. During the study period (1993-95), superficial fires occurred at all four sites. As a result, the relative frequency of the smallest plants increased due to height reductions among juveniles, while there were no changes in adult tree frequency. Analysis of changes in crown cover showed that seedlings and adult trees recovered from fire, while there were reductions in crown cover in the juvenile categories. It is concluded that although both species tolerate fires, the high frequency fire regimen prevailing in the study area may inhibit the regeneration of their populations.

scholarly journals Taking into account Qualitative and Textual Variables in Hierarchical Ascending Clustering (HAC)

2019 ◽  
Vol 8 (3) ◽  
pp. 1555-1561
Keyword(s):  
Machine Learning ◽  
Data Science ◽  
Free Text ◽  
Clustering Methods ◽  
Clustering Method ◽  
Textual Data ◽  
Different Types ◽  
Qualitative Variables ◽  
Comparison Of The Results ◽  
Numeric Attributes

In Machine Learning, the clustering methods are the mains unsupervised methods. Their objectives is to partition a set of objects in some homogeneously groups. Clustering methods in general and more particularly Hierarchical Ascending Clustering (HAC) techniques are based on metrics and ultra-metrics. Metrics are used to evaluate the similarities between two objects; and ultra-metrics are used to estimate the similarity of two groups or the similarity of an element and a group. The main characteristic of these metrics and ultra-metrics is the fact that they are only adapted to numerical variables or can be reduced to them. With the advent of Data Mining and Data Science, most of the datasets to be analyzed contain different types of variables. In the same dataset, we can find numeric attributes, qualitative variables and free text fields very often together. Despite this diversity of variables in the same dataset, the existed clustering methods are generally build to use only an unique kind of attribute. In this paper, we propose an approach to take account different types of attributes in the same clustering method. The method proposed is a variant of HAC methods that can take into account both numerical, qualitative and textual data. Our approach is based on a metric call Phi-Similarity we developed in order to estimate the proximity of two objects, each of them is describe by a vector of attributes of different types. The developed method will be implemented with the scientific computing language R and applied to real survey data. A comparison of the results will be made with HAC techniques based on classical metrics with the Ward criterion as aggregation criteria. For classical algorithms, we will limit ourselves to the variables of the database compatible with them. This work of comparison will highlight the gain in precision in terms of classification brought by our method compared to the classic versions of HAC

scholarly journals SYSTEMATIC ANALYSIS OF MULTIVARIATE SCENARIOS USING ADVANCED CLUSTERING METHODS

2021 ◽  
Vol 247 ◽  
pp. 13002
Author(s):  
Ernoult Marc ◽  
David Sylvain ◽  
Xavier Doligez ◽  
Liang Jiali ◽  
Thiolliere Nicolas ◽  
...  
Keyword(s):  
Fuel Cycle ◽  
Evaluation Criteria ◽  
Principal Component ◽  
New Method ◽  
Clustering Methods ◽  
Operational Parameters ◽  
Systematic Analysis ◽  
Multiple Variable ◽  
Comparison Of The Results ◽  
Global Values

The continuous improvement of fuel cycle simulators in conjunction with the increase of computing capacities have led to a new scale of scenario studies. Taking into consideration multiple variable parameters and observing their effect on multiple evaluation criteria, these scenario studies regroup several thousands of trajectories paving the different possible values for multiple operational parameters. If global methods like sensitivity analysis allow extracting useful information from these groups of trajectories, they only provide average and global values. In this work we present a new method to analyze these groups of trajectories while keeping some localization in the information. Based on principal component analysis, clustering method have been implemented in order to mathematically extract, from the ensemble of trajectories simulated for a scenario study, subgroups of trajectories that have similar behaviors. Typical trajectories, representative of these subgroups, are then determined. The application of this new method on a sample scenario for two different output, the total amount of transuranic elements within the fuel cycle and the number of time the MOX fuel could not be built during the simulated time, is presented. The comparison of the results between the two analyses shows that the method allows good clustering for continuous and regular outputs but struggle with discrete highly non-linear ones.

Using an Integrated Multidimensional Scaling and Clustering Method to Reduce the Number of Scenarios Based on Flow-Unit Models Under Geological Uncertainties

2020 ◽  
Vol 142 (6) ◽  
Author(s):  
Seyed Kourosh Mahjour ◽  
Manuel Gomes Correia ◽  
Antonio Alberto de Souza dos Santos ◽  
Denis José Schiozer
Keyword(s):  
Multidimensional Scaling ◽  
Negative Impact ◽  
Flow Behavior ◽  
Flow Simulation ◽  
Flow Unit ◽  
Clustering Methods ◽  
3D Flow ◽  
Geological Uncertainty ◽  
Statistical Solution ◽  
Uncertainty Range

Abstract Understanding the role of geological uncertainties on reservoir management decisions requires an ensemble of reservoir models that cover the uncertain space of parameters. However, in most cases, high computation time is needed for the flow simulation step, which can have a negative impact on a suitable assessment of flow behavior. Therefore, one important point is to choose a few scenarios from the ensemble of models while preserving the geological uncertainty range. In this study, we present a statistical solution to select the representative models (RMs) based on a novel scheme of measuring the similarity between 3D flow-unit models. The proposed method includes the integration of multidimensional scaling and cluster analysis (IMC). IMC can be applied to the models before the simulation process to save time and costs. To check the validity of the methodology, numerical simulation and then uncertainty analysis are carried out on the RMs and full set. We create an ensemble of 200 3D flow-unit models through the Latin Hypercube sampling method. The models indicate the geological uncertainty range for properties such as permeability, porosity, and net-to-gross. This method is applied to a synthetic benchmark model named UNISIM-II-D and proves to offer good performance in reducing the number of models so that only 9% of the models in the ensemble (18 selected models from 200 models) can be sufficient for the uncertainty quantification if appropriate similarity measures and clustering methods are used.

Relative Frequency Predicts Presence of Voice Disorders

ASHA Leader ◽  
2013 ◽  
Vol 18 (3) ◽  
pp. 31-31
Keyword(s):  
Relative Frequency ◽  
Voice Disorders

Relative Frequency Predicts Presence of Voice Disorders

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