Local Outlier Detection Algorithm Based on Gaussian Kernel Density Function

2019 ◽  
pp. 330-343
Author(s):  
Zhongping Zhang ◽  
Jiaojiao Liu ◽  
Chuangye Miao
Keyword(s):  
Density Function ◽  
Outlier Detection ◽  
Kernel Density ◽  
Detection Algorithm ◽  
Gaussian Kernel ◽  
Local Outlier ◽  
Kernel Density Function

scholarly journals KAPABILITAS PROSES DENGAN ESTIMASI FUNGSI DENSITAS KERNEL PADA PRODUKSI DENIM DI PT APAC INTI CORPORA

2018 ◽  
Vol 7 (3) ◽  
pp. 326-336
Author(s):  
Puput Ramadhani ◽  
Dwi Ispriyanti ◽  
Diah Safitri
Keyword(s):  
Quality Control ◽  
Production Process ◽  
Density Function ◽  
Control Chart ◽  
Cross Validation ◽  
Kernel Density ◽  
Process Capability ◽  
Conformity Index ◽  
Denim Fabric ◽  
Kernel Density Function

The quality of production becomes one of the basic factors of consumer decisions in choosing a product. Quality control is needed to control the production process. Control chart is a tool used in performing statistical quality control. One of the alternatives used when the data obtained is not known distribution is analyzed by nonparametric approach based on estimation of kernel density function. The most important thing in estimating kernel density function is optimal bandwidth selection (h) which minimizes Cross Validation (CV) value. Some of the kernel functions used in this research are Rectangular, Epanechnikov, Triangular, Biweight, and Gaussian. If the process control chart is statistically controlled, a process capability analysis can be calculated using the process conformity index to determine the nature of the process capability. In this research, the kernel control chart and process conformity index were used to analyze the slope shift of Akira-F style fabric and Corvus-SI style on the production of denim fabric at PT Apac Inti Corpora. The results of the analysis show that the production process for Akira-F style is statistically controlled, but Ypk > Yp is 0.889823 > 0,508059 indicating that the process is still not in accordance with the specified limits set by the company, while for Corvus- SI is statistically controlled and Ypk < Yp is 0.637742 < 0.638776 which indicates that the process is in accordance with the specification limits specified by the company. Keywords:     kernel density function estimation, Cross Validation, kernel control chart, denim fabric, process capability

scholarly journals A Method for Calibrating Deterministic Forecasts of Rare Events

2012 ◽  
Vol 27 (2) ◽  
pp. 531-538 ◽  
Author(s):  
Patrick T. Marsh ◽  
John S. Kain ◽  
Valliappa Lakshmanan ◽  
Adam J. Clark ◽  
Nathan M. Hitchens ◽  
...  
Keyword(s):  
Density Function ◽  
Rare Events ◽  
Numerical Models ◽  
Kernel Density ◽  
Spatial Error ◽  
Error Characteristics ◽  
Probabilistic Forecasts ◽  
Insight Into ◽  
Kernel Density Function

Abstract Convection-allowing models offer forecasters unique insight into convective hazards relative to numerical models using parameterized convection. However, methods to best characterize the uncertainty of guidance derived from convection-allowing models are still unrefined. This paper proposes a method of deriving calibrated probabilistic forecasts of rare events from deterministic forecasts by fitting a parametric kernel density function to the model’s historical spatial error characteristics. This kernel density function is then applied to individual forecast fields to produce probabilistic forecasts.

Local Outlier Detection Algorithm Based on Coefficient of Variation

2014 ◽  
Vol 635-637 ◽  
pp. 1723-1728
Author(s):  
Shi Bo Zhou ◽  
Wei Xiang Xu
Keyword(s):  
Data Mining ◽  
Outlier Detection ◽  
Coefficient Of Variation ◽  
Detection Algorithm ◽  
Experimental Results ◽  
Data Set ◽  
Outliers Detection ◽  
Deviation Factor ◽  
Local Deviation ◽  
Local Outlier

Local outliers detection is an important issue in data mining. By analyzing the limitations of the existing outlier detection algorthms, a local outlier detection algorthm based on coefficient of variation is introduced. This algorthms applies K-means which is strong in outliers searching, divides data set into sections, puts outliers and their nearing clusters into a local neighbourhood, then figures out the local deviation factor of each local neighbourhood by coefficient of variation, as a result, local outliers can more likely be found.The heoretic analysis and experimental results indicate that the method is ef fective and efficient.

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