FLDS: Fast Outlier Detection Based on Local Density Score

2016 ◽  
Author(s):  
Vu Viet Thang ◽  
D.V. Pantiukhin ◽  
A.N. Nazarov
Keyword(s):  
Outlier Detection ◽  
Local Density ◽  
Density Score

Outlier detection based on multi-dimensional clustering and local density

2017 ◽  
Vol 24 (6) ◽  
pp. 1299-1306 ◽  
Author(s):  
Zhao-yu Shou ◽  
Meng-ya Li ◽  
Si-min Li
Keyword(s):  
Outlier Detection ◽  
Local Density

scholarly journals A local density-based approach for outlier detection

2017 ◽  
Vol 241 ◽  
pp. 171-180 ◽  
Author(s):  
Bo Tang ◽  
Haibo He
Keyword(s):  
Outlier Detection ◽  
Local Density

scholarly journals Outlier Detection Method based on Adaptive Clustering Method and Density Peak

2020 ◽  
Vol 13 (6) ◽  
pp. 120-130
Author(s):  
Neelampalli Jayanthi ◽  
◽  
Burra Babu ◽  
Nandam Rao ◽  
◽  
...  
Keyword(s):  
Data Analysis ◽  
Outlier Detection ◽  
Detection Method ◽  
Local Density ◽  
Density Peak ◽  
Distance Information ◽  
Fast Search ◽  
Adaptive Clustering ◽  
Cluster Centre ◽  
Selection Of

The outlier detection technique is widely used in the data analysis for the clustering of data. Many techniques have been applied in the outlier detection to increase the efficiency of the data analysis. The Local Projection based Outlier Detection (LPOD) method effectively identifies neighbouring values of data, but this has the drawback of random selection of the cluster centre that affects the overall clustering performance of the system. In this study, the Adaptive Clustering by Fast Search and Find of Density Peak (ACFSFDP) is proposed to select the clustering centre and density peak. This ACFSFDP method is implemented with the min-max algorithm to find the number of categories that measured the local density and distance information. The density and distance are used to select the cluster centre, but density is not calculated on the existing distance based clustering techniques. The ACFSFDP method calculates cluster centre based on the density and distance during the clustering process, whereas the existing techniques randomly select the data centre. The results indicated that the ACFSFDP method is provided effective outlier detection compared with existing Clustering by Fast Search and Find of Density Peak (CFSFDP) methods. The ACFSFDP is tested on two datasets Pen-digits and waveform datasets. The experiment results proved that Area Under Curve (AUC) of the ACFSFDP is 99.08% on the Pen-Digit dataset, while the existing distance classifier method k-Nearest Neighbour has achieved 68.7% of AUC.

Ab initio band theory approach to electron energy loss near edge structures

1988 ◽  
Vol 46 ◽  
pp. 506-507
Author(s):  
Xudong Weng ◽  
O.F. Sankey ◽  
Peter Rez
Keyword(s):  
Ab Initio ◽  
Local Density ◽  
Interpolation Method ◽  
Atomic Orbital ◽  
Plane Waves ◽  
Large Set ◽  
Theory Approach ◽  
Band Theory ◽  
Atomic Orbitals ◽  
Pseudopotential Calculations

Single electron band structure techniques have been applied successfully to the interpretation of the near edge structures of metals and other materials. Among various band theories, the linear combination of atomic orbital (LCAO) method is especially simple and interpretable. The commonly used empirical LCAO method is mainly an interpolation method, where the energies and wave functions of atomic orbitals are adjusted in order to fit experimental or more accurately determined electron states. To achieve better accuracy, the size of calculation has to be expanded, for example, to include excited states and more-distant-neighboring atoms. This tends to sacrifice the simplicity and interpretability of the method.In this paper. we adopt an ab initio scheme which incorporates the conceptual advantage of the LCAO method with the accuracy of ab initio pseudopotential calculations. The so called pscudo-atomic-orbitals (PAO's), computed from a free atom within the local-density approximation and the pseudopotential approximation, are used as the basis of expansion, replacing the usually very large set of plane waves in the conventional pseudopotential method. These PAO's however, do not consist of a rigorously complete set of orthonormal states.

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