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Automated Data Filtering Approach for ANN Modeling of Distributed Energy Systems: Exploring the Application of Machine Learning
2020
Energies
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The proposed method uses Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to detect and filter out the outliers. ...
This study aims to evaluate a machine-learning-based methodology for autodetecting outliers from real data, exploring an interdisciplinary solution to replace the conventional manual approach that was ...
based on DBSCAN on the prediction accuracy of the ANNs. ...
doi:10.3390/en13143750
fatcat:b552xgej6zhpngu5wlilvvhjfm
Cluster Analysis
[chapter]
2014
Encyclopedia of Social Network Analysis and Mining
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on groups Cluster & find characteristics/patterns for each group Finding K-nearest Neighbors Localizing search to one or a small number of clusters Outlier detection: Outliers are often viewed ...
customer belongs to only one region) vs. nonexclusive (e.g., one document may belong to more than one class) Similarity measure Distance-based (e.g., Euclidian, road network, vector) vs. connectivity-based ...
(SIGMOD'98) (more grid-based)
39
Density-Based Clustering: Basic Concepts
Two parameters:
Eps: Maximum radius of the neighbourhood
MinPts: Minimum number of points in an Eps-
neighbourhood ...
doi:10.1007/978-1-4614-6170-8_100658
fatcat:se42dkhus5ahzgygbi2jmpv32y
A computational study on outliers in world music
2017
PLoS ONE
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OPEN ACCESS Citation: Panteli M, Benetos E, Dixon S (2017) A computational study on outliers in world music. PLoS ONE 12(12): e0189399. https://doi.org/ 10. ...
We use signal processing tools to extract music information from audio recordings, data mining to quantify similarity and detect outliers, and spatial statistics to account for geographical correlation ...
In this study, we focus on music dissimilarity or musical distinctiveness. In particular we aim to detect music outliers. ...
doi:10.1371/journal.pone.0189399
pmid:29253027
pmcid:PMC5734747
fatcat:7dhbkgtfy5fevj2tyu3goged24
Conformal k-NN Anomaly Detector for Univariate Data Streams
[article]
2017
arXiv
pre-print
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Despite its simplicity the method performs on par with complex prediction-based models on the Numenta Anomaly Detection benchmark and the Yahoo! S5 dataset. ...
In this paper we consider a model-free anomaly detection method for univariate time-series which adapts to non-stationarity in the data stream and provides probabilistic abnormality scores based on the ...
Distance-based anomaly detection uses a distance d on the input space X to quantify the degree of dissimilarity between observations. ...
arXiv:1706.03412v1
fatcat:zgzwdous6rg6bputsvnowwbno4
Regional assessment of the vulnerability of biotopes to landscape change
2019
Global Ecology and Conservation
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Only a few biotope groups showed a homogenous vulnerability level across their associated patches, suggesting that management based on local contexts is needed for the majority of biotopes. ...
For the 32 biotope groups that were distinguished within this study, a relative ranking of vulnerability level is provided. ...
The dissimilarity was based on the variables vulnerability score (numerical) and biotope group (categorical). ...
doi:10.1016/j.gecco.2019.e00771
fatcat:r7dlozdrcfb2xng3husbeszhoa
AUTOMATIC REFINEMENT OF TRAINING DATA FOR CLASSIFICATION OF SATELLITE IMAGERY
2012
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Therefore, a robust stopping criterion based on training data characteristics is described. ...
Outliers are detected by their newly determined class membership as well as through analysis of uncertainty of classified samples. ...
For SVM classification, further methodologies can be roughly categorised into online learning and batch learning based techniques. In online learning, samples are added one at a time. ...
doi:10.5194/isprsannals-i-7-117-2012
fatcat:7u6eblvoijetje4v7d4iych5rm
Outlier Detection for Improving Data Robust by ODAD Clustering Technique
2019
International Journal of Advanced Trends in Computer Science and Engineering
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This paper identifies the outliers in the dataset through an algorithm named outlier detection based on angle and distance based (ODAD) which is based on clustering techniques (which is combination of ...
The paper presents the concept of outliers and its detection by applying an altogether a new approach. Outliers are the odd man out data points falling under the domain of data mining. ...
It is very helpful in online fraud detection. There are two types of outliers: (i) local outliers and (ii) global outliers. ...
doi:10.30534/ijatcse/2019/130862019
fatcat:z7pmxmn735czhpknlmgruchbfu
IMPROVED DENSITY BASED ALGORITHM FOR DATA STREAM CLUSTERING
2015
Jurnal Teknologi
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This algorithm can improve the offline phase of density-based algorithm based on MinPts parameter. ...
Density-based techniques are the remarkable category of clustering techniques that are able to detect the clusters with arbitrary shapes and noises. ...
threshold relative to cmicroclusters. ...
doi:10.11113/jt.v77.6492
fatcat:apwtwh2gyzb7za6lyrd4eubply
A review of novelty detection
2014
Signal Processing
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[197] develop an efficient density-based outlier detection approach based on a relative density factor (RDF). ...
Another simple statistical scheme for outlier detection is based on the use of the box-plot rule. ...
doi:10.1016/j.sigpro.2013.12.026
fatcat:ha6kc4bzhbajxbo2mdyh5cw5hu
Are socioenvironmental factors associated with psychotic symptoms in people with first-episode psychosis? A cross-sectional study of a West London clinical sample
2019
BMJ Open
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ObjectivesTo determine whether neighbourhood-level socioenvironmental factors including deprivation and inequality predict variance in psychotic symptoms after controlling for individual-level demographics.DesignA ...
neighbourhood-level predictors, including population density, income deprivation, income inequality, social fragmentation, social cohesion, ethnic density and ethnic fragmentation, using multilevel regression ...
Finally, since our sample was characterised by relatively few participants per neighbourhood we were likely underpowered to detect random effects, particularly in view of the fact that estimates of random ...
doi:10.1136/bmjopen-2019-030448
pmid:31537571
pmcid:PMC6756588
fatcat:knwvl5glvjg27cugfxpildzxye
TweeProfiles: Detection of Spatio-temporal Patterns on Twitter
[chapter]
2014
Lecture Notes in Computer Science
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Online social networks present themselves as valuable information sources about their users and their respective behaviours and interests. ...
The data mining process that extracts the patterns is composed by the manipulation of the dissimilarity matrices for each type of data, which are fed to a clustering algorithm to obtain the desired patterns ...
. • Microblog messages contain lots of noise and using a density based approach, this noise is considered as an outlier and filtered from the results [24] . • It allows the input of a dissimilarity matrix ...
doi:10.1007/978-3-319-14717-8_10
fatcat:uqvkgllrznc2ng2uclqw7tpp3q
A Comparative Review of Outlier Detection Techniques for Wireless Sensor Networks
2017
International Journal of Recent Trends in Engineering and Research
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Additionally, it presents a technique-based classification and comparison to be used as a guideline to select a technique suitable for a particular application based on characteristics such as data type ...
Noise and errors, events, and malicious attacks on the network are the potential sources of outliers. ...
Thus, a challenge for outlier detection in WSNs is how to process distributed streaming data online. ...
doi:10.23883/ijrter.2017.3302.nebjd
fatcat:36w3qsydunhdngh4vmlv3vktwu
Benefit-based consumer segmentation and performance evaluation of clustering approaches: An evidence of data-driven decision-making
2018
Expert systems with applications
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The paper focuses on three aspects of datasets including the ordinal nature of data, high dimensionality and outliers. ...
The findings suggest that Fuzzy and SOM based clustering techniques are comparatively more efficient than traditional approaches in revealing the hidden structure in the data set. ...
Identification of outlier has many applications such as fraud detection, intrusion detection, etc., (Aggarwal and Yu, 2001) . ...
doi:10.1016/j.eswa.2018.03.007
fatcat:mtkeugp5kbbwbdhd7jxezklw3y
Automated weighted outlier detection technique for multivariate data
2018
Control Engineering Practice
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The methodology also introduces the concept of a desirability function to enable automatic decision making based on multiple statistical Automated Outlier Detection Page 2 of 34 measures for outlier detection ...
In this paper, we focus on the detection of multivariate outliers in a calibration set. ...
Wilson wrote on a statistical methodology for detecting outliers by ranking observations in order of their dissimilarity to the others in the dataset [8] . ...
doi:10.1016/j.conengprac.2017.09.018
fatcat:5babt44czvfqnmpcbtoq2lc66m
Using Deep Learning Towards Biomedical Knowledge Discovery
2017
International Journal of Mathematical Sciences and Computing
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A vast amount of knowledge exists within biomedical literature, publications, clinical notes and online content. ...
detection we found many interesting results like below: Outlier from [pain, leg, head] is pain. Outlier from [water solid, blood] is solid. ...
Additionally, the algorithm enables detecting outliers from a group of terms, for example, "cereal" is an outlier in the set "breakfast, cereal, dinner, lunch". ...
doi:10.5815/ijmsc.2017.02.01
fatcat:4jqlt4uv3bbvxjwibu3u3f3rhm
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