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Automated Data Filtering Approach for ANN Modeling of Distributed Energy Systems: Exploring the Application of Machine Learning

Homam Nikpey Somehsaraei, Susmita Ghosh, Sayantan Maity, Payel Pramanik, Sudipta De, Mohsen Assadi
2020 Energies  
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  
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

Maria Panteli, Emmanouil Benetos, Simon Dixon, Chun-Hsi Huang
2017 PLoS ONE  
OPEN ACCESS Citation: Panteli M, Benetos E, Dixon S (2017) A computational study on outliers in world music. PLoS ONE 12(12): e0189399. 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]

Vladislav Ishimtsev, Ivan Nazarov, Alexander Bernstein, Evgeny Burnaev
2017 arXiv   pre-print
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

Peter Weißhuhn
2019 Global Ecology and Conservation  
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


T. Büschenfeld, J. Ostermann
2012 ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
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

Deepti Mishra, NIU, Greater Noida India
2019 International Journal of Advanced Trends in Computer Science and Engineering  
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


Maryam Mousavi, Azuraliza Abu Bakar
2015 Jurnal Teknologi  
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

Marco A.F. Pimentel, David A. Clifton, Lei Clifton, Lionel Tarassenko
2014 Signal Processing  
[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

Marc S Tibber, James B Kirkbride, Stanley Mutsatsa, Isobel Harrison, Thomas R E Barnes, Eileen M Joyce, Vyv Huddy
2019 BMJ Open  
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]

Tiago Cunha, Carlos Soares, Eduarda Mendes Rodrigues
2014 Lecture Notes in Computer Science  
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  
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

Deepak Arunachalam, Niraj Kumar
2018 Expert systems with applications  
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

Suresh N. Thennadil, Mark Dewar, Craig Herdsman, Alison Nordon, Edo Becker
2018 Control Engineering Practice  
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

Nadeem N. Rather, Chintan O. Patel, Sharib A. Khan
2017 International Journal of Mathematical Sciences and Computing  
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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