scholarly journals HYBCIM: Hypercube Based Cluster Initialization Method for k-means

2019 ◽  
Vol 8 (10) ◽  
pp. 3584-3587 ◽  
Keyword(s):  
Clustering Algorithms ◽  
Major Effect ◽  
Processing Technique ◽  
Quality Parameters ◽  
Data Sets ◽  
Initial Cluster ◽  
And Performance ◽  
Initial Choice ◽  
Accuracy Performance ◽  
Data Processing Technique

Clustering is a data processing technique that is extensively used to find novel patterns in data in the field of data mining and also in classification techniques. The k-means algorithm is extensively used for clustering due to its ease and reliability. A major effect on the accuracy and performance of the k-means algorithm is by the initial choice of the cluster centroids. Minimizing Sum of Squares of the distance from the centroid of the cluster for cluster points within the cluster (SSW) and maximizing Sum of Square distance between the centroids of different clusters (SSB) are two generally used quality parameters of the clustering technique. To improve the accuracy, performance and quality parameters of the k-means algorithm, a new Hypercube Based Cluster Initialization Method, called HYBCIM, is proposed in this work. In the proposed method, collection of k equi-sized partitions of all dimensions is modeled as a hypercube. The motivation behind the proposed method is that the clusters may spread horizontally, vertically, diagonally or in arc shaped. The proposed method empirically evaluated on four popular data sets. The results show that the proposed method is superior to basic k-means. HYBCIM is applicable for clustering both discrete and continuous data. Though, HYBCIM is proposed for k-means but it can also be applied with other clustering algorithms which are based on initial cluster centroids.

scholarly journals Image Processing of SEM Image Nano Silver Using K-means MATLAB Technique

2019 ◽  
Vol 29 (3) ◽  
pp. 150 ◽  
Author(s):  
Elham Jasim Mohammad
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Clustering Algorithms ◽  
Small Data ◽  
Data Sets ◽  
Data Set ◽  
Sem Image ◽  
Huge Data ◽  
And Performance ◽  
Biometric Information

Nanotechnology is one of the non-exhaustive applications in which image processing is used. For optimal nanoparticle visualization and characterization, the high resolution Scanning Electron Microscope (SEM) and the Atomic Force Microscope (AFM) are used. Image segmentation is one of the critical steps in nanoscale processing. There are also different ways to reach retail, including statistical approximations.In this study; we used the K-means method to determine the optimal threshold using statistical approximation. This technique is thoroughly studied for the SEM nanostructure Silver image. Note that, the image obtained by SEM is good enough to analyze more recently images. The analysis is being used in the field of nanotechnology. The K-means algorithm classifies the data set given to k groups based on certain measurements of certain distances. K-means technology is the most widely used among all clustering algorithms. It is one of the common techniques used in statistical data analysis, image analysis, neural networks, classification analysis and biometric information. K-means is one of the fastest collection algorithms and can be easily used in image segmentation. The results showed that K-means is highly sensitive to small data sets and performance can degrade at any time. When exposed to a huge data set such as 100.000, the performance increases significantly. The algorithm also works well when the number of clusters is small. This technology has helped to provide a good performance algorithm for the state of the image being tested.

ANALISIS IMPLEMENTASI ANGGARAN BERBASIS KINERJA TERHADAP AKUNTABILITAS KINERJA PEMERINTAH KABUPATEN PEGUNUNGAN BINTANG

2021 ◽  
Vol 3 (2) ◽  
pp. 67-74
Author(s):  
Vicky Antonio Prayoga ◽  
Siti Rofingatun ◽  
Sylvia Christina Daat
Keyword(s):  
Performance Evaluation ◽  
Sampling Method ◽  
Hypothesis Test ◽  
Processing Technique ◽  
Cluster Sampling ◽  
Reliability Test ◽  
Normality Test ◽  
Performance Accountability ◽  
And Performance ◽  
Data Processing Technique

The objective of this study is to observe the influence of performance-based budgeting (PBB) on thegovernment's accountability, such as planning, implementation, accountability, and performanceevaluation. Pegunungan Bintang was chosen as a research location and the cluster sampling method was used to determined 96 respondents. Data processing technique consist of several stages, first stage isdata quality test by used validity test and reliability test, next step was normality test,multicollinearity test dan heteroscedasticity test called classical assumption test, when researchdata was valid and reliable and normally distributed, there was no multicollinearity danheteroscedasticity, then hypothesis test worth to continue. Hypothesis test consisted of multiplelinear regression, determination coefficient (R2), partial tested (t-test), and simultaneous tested (Ftest).The result shows that partially the planed and implementation have no effect on accountability ofgovernment’s performance accountability with significance values of 0.872 and 0.656 respectively,however accountability and performance evaluation had an effect on the government’s performanceaccountability with significance values of 0,000 and 0,000.

scholarly journals PENGARUH KEMAMPUAN KERJA DAN PROFESIONALISME PEGAWAI TERHADAP KINERJA PEGAWAI PADA DINAS PARIWISATA KABUPATEN BALANGAN PROVINSI KALIMANTAN SELATAN

KINDAI ◽  
2021 ◽  
Vol 17 (2) ◽  
pp. 291-302
Author(s):  
Yudhi Arisa Putra
Keyword(s):  
Regression Analysis ◽  
Work Ability ◽  
Quantitative Methods ◽  
Employee Performance ◽  
Processing Technique ◽  
Service Employees ◽  
And Performance ◽  
Tourism Service ◽  
Data Processing Technique ◽  
Dominant Influence

PENGARUH KEMAMPUAN KERJA DAN PROFESIONALISME PEGAWAI TERHADAP KINERJA PEGAWAI PADA DINAS PARIWISATA KABUPATEN BALANGAN PROVINSI KALIMANTAN SELATAN   Yudhi Arisa Putra Sekolah Tinggi Ilmu Ekonomi Pancasetia Banjarmasin Jl. Ahmad Yani Km. 5.5 Banjarmasin [email protected]     Abstrak: Yudhi Arisa Putra, Npm. 1911.32202.5174, Pengaruh Kemampuan Kerja Dan Profesionalisme Pegawai Terhadap Kinerja Pegawai pada Dinas Pariwisata Kabupaten Balangan Provinsi Kalimantan Selatan Dibawah Bimbingan Dr. Lanny Purnama Kosasi,MM Dan Drs.M.Zaid Abdurakhman, MM, 2021. Tujuan penelitian untuk mengetahui pengaruh kemampuan kerja dan profesionalisme pegawai secara simultan terhadap kinerja pegawai, untuk mengetahui pengaruh kemampuan kerja dan profesionalisme pegawai secara parsial terhadap kinerja pegawai dan untuk mengetahui diantara kemampuan kerja dan profesionalisme pegawai yang berpengaruh dominan terhadap kinerja pegawai Dinas Pariwisata Kabupaten Balangan Propinsi Kalimantan Selatan. Populasi dan sampel dalam penelitian ini ditentukan sebanyak 40 respoden. Teknik pengolahan data yang akan dilakukan menggunakan metode  kuantitatif dengan analisis regresi berganda.Hasil penelitiakemampuan kerja dan profesionalisme pegawai  berpengaruh signifikan secara simultan terhadap kinerja pegawai, kemampuan kerja dan profesionalisme pegawai  berpengaruh signifikan secara parsial terhadap kinerja pegawai dan profesionalisme pegawai berpengaruh dominan terhadap kinerja pegawai pada Dinas Pariwisata Kabupaten Balangan Propinsi Kalimantan Selatan.   Kata Kunci : Kemampuan Kerja, Profesionalisme, dan Kinerja.   Abstract: Yudhi Arisa Putra, Npm. 1911.32202.5174, Effect Of Employee Ability And Employee Professionalism On The Performance Of Tourism Service Employees In Balangan Regency Province South Kalimantan, Under Guidance Dr.Lanny Purnama Kosasi,MM and Drs.M.Zaid Abdurakhman,MM, 2021. The purpose of the study was to determine the effect of work ability and professionalism of employees simultaneously on employee performance, to determine the effect of work ability and professionalism of employees partially on employee performance and to find out between work ability and employee professionalism which had a dominant influence on employee performance at the Tourism Office of Balangan Regency, Kalimantan Province. South. The population and sample in this study were determined as many as 40 respondents. The data processing technique will be carried out using quantitative methods with multiple regression analysis. The results of the research that work ability and employee professionalism have a significant simultaneous effect on employee performance, work ability and employee professionalism have a partially significant effect on employee performance and employee professionalism has a dominant influence on employee performance at the Tourism Office of Balangan Regency, South Kalimantan Province.   Keywords: Work Ability, Professionalism, and Performance

scholarly journals MP-K-Means: Modified Partition Based Cluster Initialization Method for K-Means Algorithm

2019 ◽  
Vol 8 (4) ◽  
pp. 1140-1148 ◽  
Keyword(s):  
Clustering Algorithms ◽  
Empirical Evaluation ◽  
Arithmetic Average ◽  
Empirical Results ◽  
Initial Cluster ◽  
And Performance ◽  
Comparative Results ◽  
Clustering Validation ◽  
Range Of Values ◽  
Selection Of

In k-means algorithm, initial cluster centroids are selected arbitrarily which leads to diverse formation of clusters in each run. Consequently, accuracy and performance of k-means is majorly depends on the selection of initial centroids. Thus, the initial cluster centroids shall be chosen carefully to obtain better accuracy and performance of k-means algorithm. In view of this, a new Modified Partition based Cluster Initialization method for k-means called as MP-k-means is proposed in this paper. MP-k-means is an amended version of P-k-means [1] in which the range of values of each dimension is divided into ‘k’ equi-sized partition based on arithmetic average. This division of range into ‘k’ equi-sized partition is affected by outliers present in the data. In order to remove the effect of outliers in P-k-means, the partitioning of each dimension is made based on positional average instead of arithmetic average in MP-k-means. Six popular datasets are used for empirical evaluation of the algorithms. The empirical results are compared and validated based on various external and internal clustering validation measures. The comparative results show that MP-k-means is significantly superior to the basic k-means and P-k-means. The proposed method may also be applied to other clustering algorithms which are based on the concept of selection of initial cluster centroids.

The Frequency of Occurrence of Handwriting Performance Features Used to Predict Whether Questioned Signatures are Simulated

2018 ◽  
Vol 28 ◽  
pp. 35-42
Author(s):  
David Black ◽  
Bryan Found ◽  
Doug Rogers
Keyword(s):  
Large Population ◽  
Quality Parameters ◽  
Frequency Of Occurrence ◽  
Relative Speed ◽  
Simulation Process ◽  
Spatial Features ◽  
And Performance ◽  
Document Examiners ◽  
Line Trace

Forensic Document Examiners (FDEs) examine the physical morphology and performance attributes of a line trace when comparing questioned to specimen handwriting samples for the purpose of determining authorship. Along with spatial features, the elements of execution of the handwriting are thought to provide information as to whether or not a questioned sample is the product of a disguise or simulation process. Line features such as tremor, pen-lifts, blunt beginning and terminating strokes, indicators of relative speed, splicing and touch ups, are subjectively assessed and used in comparisons by FDEs and can contribute to the formation of an opinion as to the validity of a questioned sample of handwriting or signatures. In spite of the routine use of features such as these, there is little information available regarding the relative frequency of occurrence of these features in populations of disguised and simulated samples when compared to a large population of a single individual’s signature. This study describes a survey of the occurrence of these features in 46 disguised signatures, 620 simulated signatures (produced by 31 different amateur forgers) and 177 genuine signatures. It was found that the presence of splices and touch-ups were particularly good predictors of the simulation process and that all line quality parameters were potentially useful contributors in the determination of the authenticity of questioned signatures. Purchase Article - $10

User Power Behavior Similarity Clustering Based on Unsupervised Extreme Learning Machine Algorithm

2020 ◽  
Vol 13 (5) ◽  
pp. 641-649
Author(s):  
Yuancheng Li ◽  
Yaqi Cui ◽  
Xiaolong Zhang
Keyword(s):  
Extreme Learning Machine ◽  
Clustering Algorithm ◽  
Characteristic Curve ◽  
Clustering Algorithms ◽  
Data Sets ◽  
Residential Areas ◽  
Processing Power ◽  
Learning Machine ◽  
Advanced Metering ◽  
Matlab Programming

Background: Advanced Metering Infrastructure (AMI) for the smart grid is growing rapidly which results in the exponential growth of data collected and transmitted in the device. By clustering this data, it can give the electricity company a better understanding of the personalized and differentiated needs of the user. Objective: The existing clustering algorithms for processing data generally have some problems, such as insufficient data utilization, high computational complexity and low accuracy of behavior recognition. Methods: In order to improve the clustering accuracy, this paper proposes a new clustering method based on the electrical behavior of the user. Starting with the analysis of user load characteristics, the user electricity data samples were constructed. The daily load characteristic curve was extracted through improved extreme learning machine clustering algorithm and effective index criteria. Moreover, clustering analysis was carried out for different users from industrial areas, commercial areas and residential areas. The improved extreme learning machine algorithm, also called Unsupervised Extreme Learning Machine (US-ELM), is an extension and improvement of the original Extreme Learning Machine (ELM), which realizes the unsupervised clustering task on the basis of the original ELM. Results: Four different data sets have been experimented and compared with other commonly used clustering algorithms by MATLAB programming. The experimental results show that the US-ELM algorithm has higher accuracy in processing power data. Conclusion: The unsupervised ELM algorithm can greatly reduce the time consumption and improve the effectiveness of clustering.

scholarly journals The International Radiomics Platform – An Initiative of the German and Austrian Radiological Societies

2020 ◽  
Author(s):  
Daniel Overhoff ◽  
Peter Kohlmann ◽  
Alex Frydrychowicz ◽  
Sergios Gatidis ◽  
Christian Loewe ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Image Data ◽  
Quality Parameters ◽  
Image Features ◽  
Performance Criteria ◽  
Data Sets ◽  
Proof Of Concept ◽  
Concept Study ◽  
Artificial Intelligence Methods

Purpose The DRG-ÖRG IRP (Deutsche Röntgengesellschaft-Österreichische Röntgengesellschaft international radiomics platform) represents a web-/cloud-based radiomics platform based on a public-private partnership. It offers the possibility of data sharing, annotation, validation and certification in the field of artificial intelligence, radiomics analysis, and integrated diagnostics. In a first proof-of-concept study, automated myocardial segmentation and automated myocardial late gadolinum enhancement (LGE) detection using radiomic image features will be evaluated for myocarditis data sets. Materials and Methods The DRG-ÖRP IRP can be used to create quality-assured, structured image data in combination with clinical data and subsequent integrated data analysis and is characterized by the following performance criteria: Possibility of using multicentric networked data, automatically calculated quality parameters, processing of annotation tasks, contour recognition using conventional and artificial intelligence methods and the possibility of targeted integration of algorithms. In a first study, a neural network pre-trained using cardiac CINE data sets was evaluated for segmentation of PSIR data sets. In a second step, radiomic features were applied for segmental detection of LGE of the same data sets, which were provided multicenter via the IRP. Results First results show the advantages (data transparency, reliability, broad involvement of all members, continuous evolution as well as validation and certification) of this platform-based approach. In the proof-of-concept study, the neural network demonstrated a Dice coefficient of 0.813 compared to the expert's segmentation of the myocardium. In the segment-based myocardial LGE detection, the AUC was 0.73 and 0.79 after exclusion of segments with uncertain annotation.The evaluation and provision of the data takes place at the IRP, taking into account the FAT (fairness, accountability, transparency) and FAIR (findable, accessible, interoperable, reusable) criteria. Conclusion It could be shown that the DRG-ÖRP IRP can be used as a crystallization point for the generation of further individual and joint projects. The execution of quantitative analyses with artificial intelligence methods is greatly facilitated by the platform approach of the DRG-ÖRP IRP, since pre-trained neural networks can be integrated and scientific groups can be networked.In a first proof-of-concept study on automated segmentation of the myocardium and automated myocardial LGE detection, these advantages were successfully applied.Our study shows that with the DRG-ÖRP IRP, strategic goals can be implemented in an interdisciplinary way, that concrete proof-of-concept examples can be demonstrated, and that a large number of individual and joint projects can be realized in a participatory way involving all groups. Key Points:  Citation Format

A Reliability-Based Approach to Assess Fatigue Behaviour Based on Small Incomplete Data Sets

2008 ◽  
Vol 44-46 ◽  
pp. 871-878 ◽  
Author(s):  
Chu Yang Luo ◽  
Jun Jiang Xiong ◽  
R.A. Shenoi
Keyword(s):  
Incomplete Data ◽  
Standard Procedure ◽  
Fatigue Behaviour ◽  
Fatigue Tests ◽  
Small Sample ◽  
Data Sets ◽  
Safe Life ◽  
A New Technique ◽  
And Performance

This paper outlines a new technique to address the paucity of data in determining fatigue life and performance based on reliability concepts. Two new randomized models are presented for estimating the safe life and pS-N curve, by using the standard procedure for statistical analysis and dealing with small sample numbers of incomplete data. The confidence level formulations for the safe and p-S-N curve are also given. The concepts are then applied for the determination of the safe life and p-S-N curve. Two sets of fatigue tests for the safe life and p-S-N curve are conducted to validate the presented method, demonstrating the practical use of the proposed technique.

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