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Journal articles on the topic 'Outliers'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Seo, Han Son. "Outlier tests on potential outliers." Korean Journal of Applied Statistics 30, no. 1 (2017): 159–67. http://dx.doi.org/10.5351/kjas.2017.30.1.159.

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Huda, Nur'ainul Miftahul, Utriweni Mukhaiyar, and Nurfitri Imro'ah. "AN ITERATIVE PROCEDURE FOR OUTLIER DETECTION IN GSTAR(1;1) MODEL." BAREKENG: Jurnal Ilmu Matematika dan Terapan 16, no. 3 (2022): 975–84. http://dx.doi.org/10.30598/barekengvol16iss3pp975-984.

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Outliers are observations that differ significantly from others that can affect the estimation results in the model and reduce the estimator's accuracy. To deal with outliers is to remove outliers from the data. However, sometimes important information is contained in the outlier, so eliminating outliers is a misinterpretation. There are two types of outliers in the time series model, Innovative Outlier (IO) and Additive Outlier (AO). In the GSTAR model, outliers and spatial and time correlations can also be detected. We introduce an iterative procedure for detecting outliers in the GSTAR mode

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., Srividya, S. Mohanavalli, N. Sripriya, and S. Poornima. "Outlier Detection using Clustering Techniques." International Journal of Engineering & Technology 7, no. 3.12 (2018): 813. http://dx.doi.org/10.14419/ijet.v7i3.12.16508.

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An outlier is nothing but a pattern that is different compared to the other existing patterns in a particular dataset. In some applications it is very important to understand and identify outliers. Detecting outlier is of major importance in many of the fields like cybersecurity, machine learning, finance, healthcare, etc., A clustering based method is proposed to detect outliers using different algorithms like k means, PAM, Clara, DBScan and LOF on different data sets like breast cancer, heart diseases, multi shaped datasets. This work aims to identify the best suitable method to detect the o

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Agyemang, Malik, Ken Barker, and Reda Alhajj. "Web outlier mining: Discovering outliers from web datasets1." Intelligent Data Analysis 9, no. 5 (2005): 473–86. http://dx.doi.org/10.3233/ida-2005-9505.

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Muhima, Rani Rotul, Muchamad Kurniawan, and Oktavian Tegar Pambudi. "A LOF K-Means Clustering on Hotspot Data." International Journal of Artificial Intelligence & Robotics (IJAIR) 2, no. 1 (2020): 29. http://dx.doi.org/10.25139/ijair.v2i1.2634.

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K-Means is the most popular of clustering method, but its drawback is sensitivity to outliers. This paper discusses the addition of the outlier removal method to the K-Means method to improve the performance of clustering. The outlier removal method was added to the Local Outlier Factor (LOF). LOF is the representative outlier’s detection algorithm based on density. In this research, the method is called LOF K-Means. The first applying clustering by using the K-Means method on hotspot data and then finding outliers using the LOF method. The object detected outliers are then removed. Then new c

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Mishra, Deepti, and Devpriya Soni. "Outliers in Data Mining: Approaches and Detection." International Journal of Engineering & Technology 7, no. 4.39 (2018): 189–98. http://dx.doi.org/10.14419/ijet.v7i4.39.23930.

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The paper is grounded on the study of outliers which are the objects that somehow arise unlike from residue data stored and can be pointed as outliers. At present in data mining Outlier detection is the currently innovative topic for research. Outliers detection in a set of patterns is a pertinent problem in the data mining area. Outlier mining is the problem of detecting unseen events, abnormal data and exceptions. Another perspective of outliers they affect the outcomes and analysis of data. Presence of outliers make the results in confusable state. The patterns generated after the calculati

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Syed Abd Mutalib, Sharifah Sakinah, Siti Zanariah Satari, and Wan Nur Syahidah Wan Yusoff. "SYNTHETIC MULTIVARIATE DATA GENERATION PROCEDURE WITH VARIOUS OUTLIER SCENARIOS USING R PROGRAMMING LANGUAGE." Jurnal Teknologi 84, no. 3 (2022): 89–101. http://dx.doi.org/10.11113/jurnalteknologi.v84.17900.

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A synthetic data generation procedure is a procedure to generate data from either a statistical or mathematical model. The data generation procedure has been used in simulation studies to compare statistical performance methods or propose a new statistical method with a specific distribution. A synthetic multivariate data generation procedure with various outlier scenarios using R is formulated in this study. An outlier generating model is used to generate multivariate data that contains outliers. Data generation procedures for various outlier scenarios by using R are explained. Three outlier

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Lartey, Clement, Jixue Liu, Richmond K. Asamoah, Christopher Greet, Massimiliano Zanin, and William Skinner. "Effective Outlier Detection for Ensuring Data Quality in Flotation Data Modelling Using Machine Learning (ML) Algorithms." Minerals 14, no. 9 (2024): 925. http://dx.doi.org/10.3390/min14090925.

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Froth flotation, a widely used mineral beneficiation technique, generates substantial volumes of data, offering the opportunity to extract valuable insights from these data for production line analysis. The quality of flotation data is critical to designing accurate prediction models and process optimisation. Unfortunately, industrial flotation data are often compromised by quality issues such as outliers that can produce misleading or erroneous analytical results. A general approach is to preprocess the data by replacing or imputing outliers with data values that have no connection with the r

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Parrinello, Christina M., Morgan E. Grams, Yingying Sang, et al. "Iterative Outlier Removal: A Method for Identifying Outliers in Laboratory Recalibration Studies." Clinical Chemistry 62, no. 7 (2016): 966–72. http://dx.doi.org/10.1373/clinchem.2016.255216.

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Abstract BACKGROUND Extreme values that arise for any reason, including those through nonlaboratory measurement procedure-related processes (inadequate mixing, evaporation, mislabeling), lead to outliers and inflate errors in recalibration studies. We present an approach termed iterative outlier removal (IOR) for identifying such outliers. METHODS We previously identified substantial laboratory drift in uric acid measurements in the Atherosclerosis Risk in Communities (ARIC) Study over time. Serum uric acid was originally measured in 1990–1992 on a Coulter DACOS instrument using an uricase-bas

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Yulistiani, Selma, and Suliadi Suliadi. "Deteksi Pencilan pada Model ARIMA dengan Bayesian Information Criterion (BIC) Termodifikasi." STATISTIKA: Journal of Theoretical Statistics and Its Applications 19, no. 1 (2019): 29–37. http://dx.doi.org/10.29313/jstat.v19i1.4740.

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Time series data may be affected by special events or circumstances such as promotions, natural disasters, etc. These events can lead to inconsistent observations in the series called outliers. Because outliers can make invalid conclusions, it is important to carry out procedures in detecting outlier effects. In outlier detection there is one type of outlier, namely additive outlier (AO). The process of detecting additive outliers in the ARIMA model can be said as a model selection problem, where the candidate model assumes additive outliers at a certain time. In the selection of models there

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Knight, Nathan L., and Jinling Wang. "A Comparison of Outlier Detection Procedures and Robust Estimation Methods in GPS Positioning." Journal of Navigation 62, no. 4 (2009): 699–709. http://dx.doi.org/10.1017/s0373463309990142.

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With more satellite systems becoming available there is currently a need for Receiver Autonomous Integrity Monitoring (RAIM) to exclude multiple outliers. While the single outlier test can be applied iteratively, in the field of statistics robust methods are preferred when multiple outliers exist. This study compares the outlier test and numerous robust methods with simulated GPS measurements to identify which methods have the greatest ability to correctly exclude outliers. It was found that no method could correctly exclude outliers 100% of the time. However, for a single outlier the outlier

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Maia Lima, Luís Fernando, Alexandre Masson Maroldi, Dávilla Vieira Odízio da Silva, Carlos Roberto Massao Hayashi, and Maria Cristina Piumbato Innocentini Hayashi. "A influência de outliers nos estudos métricos da informação: uma análise de dados univariados." Em Questão 24 (December 31, 2018): 216. http://dx.doi.org/10.19132/1808-5245240.216-235.

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Este artigo apresenta uma nova fórmula de detecção de outliers via Análise Exploratória de Dados, levando em conta a assimetria dos dados, e também estuda o efeito da remoção dos outliers dos dados originais. Aplica-se a fórmula para três conjuntos de dados publicados na literatura de estudos métricos da informação. O primeiro conjunto de dados apresenta cinco outliers inferiores. A média, dos dados agregados, conduz à falsa impressão de que 40 universidades, de um total de 49, estão acima da média. A remoção dos cinco outliers inferiores conduz a uma nova média em que somente 22 universidades

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Hasanah, Siti Tabi'atul. "Pendeteksian Outlier pada Regresi Nonlinier dengan Metode statistik Likelihood Displacement." CAUCHY 2, no. 3 (2012): 177. http://dx.doi.org/10.18860/ca.v2i3.3127.

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<div class="standard"><a id="magicparlabel-1713">Outlier is an observation that much different (extreme) from the other observational data, or data can be interpreted that do not follow the general pattern of the model. Sometimes outliers provide information that can not be provided by other data. That's why outliers should not just be eliminated. Outliers can also be an influential observation. There are many methods that can be used to detect of outliers. In previous studies done on outlier detection of linear regression. Next will be developed detection of outliers in nonlinear

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Zhao, Xi, Yun Zhang, Shoulie Xie, Qianqing Qin, Shiqian Wu, and Bin Luo. "Outlier Detection Based on Residual Histogram Preference for Geometric Multi-Model Fitting." Sensors 20, no. 11 (2020): 3037. http://dx.doi.org/10.3390/s20113037.

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Geometric model fitting is a fundamental issue in computer vision, and the fitting accuracy is affected by outliers. In order to eliminate the impact of the outliers, the inlier threshold or scale estimator is usually adopted. However, a single inlier threshold cannot satisfy multiple models in the data, and scale estimators with a certain noise distribution model work poorly in geometric model fitting. It can be observed that the residuals of outliers are big for all true models in the data, which makes the consensus of the outliers. Based on this observation, we propose a preference analysis

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Fitrianto, Anwar, Wan Zuki Azman Wan Muhamad, Suliana Kriswan, and Budi Susetyo. "Comparing Outlier Detection Methods using Boxplot Generalized Extreme Studentized Deviate and Sequential Fences." Aceh International Journal of Science and Technology 11, no. 1 (2022): 38–45. http://dx.doi.org/10.13170/aijst.11.1.23809.

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Outliers identification is essential in data analysis since it can make wrong inferential statistics. This study aimed to compare the performance of Boxplot, Generalized Extreme Studentized Deviate (Generalized ESD), and Sequential Fences method in identifying outliers. A published dataset was used in the study. Based on preliminary outlier identification, the data did not contain outliers. Each outlier detection method's performance was evaluated by contaminating the original data with few outliers. The contaminations were conducted by replacing the two smallest and largest observations with

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He, Zengyou, Xiaofei Xu, Zhexue Huang, and Shengchun Deng. "FP-outlier: Frequent pattern based outlier detection." Computer Science and Information Systems 2, no. 1 (2005): 103–18. http://dx.doi.org/10.2298/csis0501103h.

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An outlier in a dataset is an observation or a point that is considerably dissimilar to or inconsistent with the remainder of the data. Detection of such outliers is important for many applications and has recently attracted much attention in the data mining research community. In this paper, we present a new method to detect outliers by discovering frequent patterns (or frequent itemsets) from the data set. The outliers are defined as the data transactions that contain less frequent patterns in their itemsets. We define a measure called FPOF (Frequent Pattern Outlier Factor) to detect the out

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Syed Abd Mutalib, Sharifah Sakinah, Siti Zanariah Satari, and Wan Nur Syahidah Wan Yusoff. "Simulation Study of the Test on Covariance Estimator for Outlier Detection in Multivariate Data with Mean and Covariance Shifts." Malaysian Journal of Fundamental and Applied Sciences 21, no. 2 (2025): 1860–73. https://doi.org/10.11113/mjfas.v21n2.3660.

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Outlier detection in multivariate data is complex compared to univariate data, which can be done using graphical inspection. Outlier detection is also one of the common issues in multivariate analysis and has been applied to tax fraud detection and industrial food inspection. Outliers’ studies are closely related to robust estimators of the sample mean and covariance matrix as these estimators are resistant toward outliers. The Test on Covariance (TOC) is a newly developed robust estimator for multivariate data. Until now, TOC’s performance was investigated for two outlier scenarios by shiftin

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Massarweh, Nader N., Chung-Yuan Hu, Y. Nancy You, et al. "Risk-Adjusted Pathologic Margin Positivity Rate As a Quality Indicator in Rectal Cancer Surgery." Journal of Clinical Oncology 32, no. 27 (2014): 2967–74. http://dx.doi.org/10.1200/jco.2014.55.5334.

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Purpose Margin positivity after rectal cancer resection is associated with poorer outcomes. We previously developed an instrument for calculating hospital risk-adjusted margin positivity rate (RAMP) that allows identification of performance-based outliers and may represent a rectal cancer surgery quality metric. Methods This was an observational cohort study of patients with rectal cancer within the National Cancer Data Base (2003 to 2005). Hospital performance was categorized as low outlier (better than expected), high outlier (worse than expected), or non-RAMP outlier using standard observed

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Cram, Peter, Xin Lu, Stephen L. Kates, Yue Li, and Benjamin J. Miller. "Outliers." Geriatric Orthopaedic Surgery & Rehabilitation 2, no. 4 (2011): 135–47. http://dx.doi.org/10.1177/2151458511419847.

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Twumasi-Ankrah, Sampson, Simon Kojo Appiah, Doris Arthur, Wilhemina Adoma Pels, Jonathan Kwaku Afriyie, and Danielson Nartey. "Comparison of outlier detection techniques in non-stationary time series data." Global Journal of Pure and Applied Sciences 27, no. 1 (2021): 55–60. http://dx.doi.org/10.4314/gjpas.v27i1.7.

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This study examined the performance of six outlier detection techniques using a non-stationary time series dataset. Two key issues were of interest. Scenario one was the method that could correctly detect the number of outliers introduced into the dataset whiles scenario two was to find the technique that would over detect the number of outliers introduced into the dataset, when a dataset contains only extreme maxima values, extreme minima values or both. Air passenger dataset was used with different outliers or extreme values ranging from 1 to 10 and 40. The six outlier detection techniques u

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Johansen, Martin Berg, and Peter Astrup Christensen. "A simple transformation independent method for outlier definition." Clinical Chemistry and Laboratory Medicine (CCLM) 56, no. 9 (2018): 1524–32. http://dx.doi.org/10.1515/cclm-2018-0025.

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AbstractBackground:Definition and elimination of outliers is a key element for medical laboratories establishing or verifying reference intervals (RIs). Especially as inclusion of just a few outlying observations may seriously affect the determination of the reference limits. Many methods have been developed for definition of outliers. Several of these methods are developed for the normal distribution and often data require transformation before outlier elimination.Methods:We have developed a non-parametric transformation independent outlier definition. The new method relies on drawing reprodu

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Insha, Altaf. "RELATIVE STUDY OF OUTLIER DETECTION PROCEDURES." International Journal of Engineering Sciences & Research Technology 5, no. 2 (2016): 685–97. https://doi.org/10.5281/zenodo.46489.

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Data Mining just alludes to the extraction of exceptionally intriguing patterns of the data from the monstrous data sets. Outlier detection is one of the imperative parts of data mining which Rexall discovers the perceptions that are going amiss from the normal expected conduct. Outlier detection and investigation is once in a while known as Outlier mining. In this paper, we have attempted to give the expansive and a far reaching literature survey of Outliers and Outlier detection procedures under one rooftop, to clarify the lavishness and multifaceted nature connected with each Outlier detect

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Read, Randy J. "Detecting outliers in non-redundant diffraction data." Acta Crystallographica Section D Biological Crystallography 55, no. 10 (1999): 1759–64. http://dx.doi.org/10.1107/s0907444999008471.

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Outliers are observations which are very unlikely to be correct, as judged by independent observations or other prior information. Such unexpected observations are treated, effectively, as being more informative about possible models, so they can seriously impede the course of structure determination and refinement. The best way to detect and eliminate outliers is to collect highly redundant data, but it is not always possible to make multiple measurements of every reflection. For non-redundant data, the prior expectation given either by a Wilson distribution of intensities or model-based stru

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Adikaram, K. K. L. B., M. A. Hussein, M. Effenberger, and T. Becker. "Outlier Detection Method in Linear Regression Based on Sum of Arithmetic Progression." Scientific World Journal 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/821623.

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We introduce a new nonparametric outlier detection method for linear series, which requires no missing or removed data imputation. For an arithmetic progression (a series without outliers) withnelements, the ratio (R) of the sum of the minimum and the maximum elements and the sum of all elements is always2/n:(0,1].R≠2/nalways implies the existence of outliers. Usually,R<2/nimplies that the minimum is an outlier, andR>2/nimplies that the maximum is an outlier. Based upon this, we derived a new method for identifying significant and nonsignificant outliers, separately. Two different techni

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Chung, Se Yeon, and Sang Cheol Kim. "Anomaly Detection in Livestock Environmental Time Series Data Using LSTM Autoencoders: A Comparison of Performance Based on Threshold Settings." Korean Institute of Smart Media 13, no. 4 (2024): 48–56. http://dx.doi.org/10.30693/smj.2024.13.4.48.

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In the livestock industry, detecting environmental outliers and predicting data are crucial tasks. Outliers in livestock environment data, typically gathered through time-series methods, can signal rapid changes in the environment and potential unexpected epidemics. Prompt detection and response to these outliers are essential to minimize stress in livestock and reduce economic losses for farmers by early detection of epidemic conditions. This study employs two methods to experiment and compare performances in setting thresholds that define outliers in livestock environment data outlier detect

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Al. Abri, Khoula, and Manjit Singh Sidhu. "Machine Learning Approaches to Advanced Outlier Detection in Psychological Datasets." International journal of electrical and computer engineering systems 15, no. 1 (2024): 13–20. http://dx.doi.org/10.32985/ijeces.15.1.2.

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The core aim of this study is to determine the most effective outlier detection methodologies for multivariate psychological datasets, particularly those derived from Omani students. Due to their complex nature, such datasets demand robust analytical methods. To this end, we employed three sophisticated algorithms: local outlier factor (LOF), one-class support vector machine (OCSVM), and isolation forest (IF). Our initial findings showed 155 outliers by both LOF and IF and 147 by OCSVM. A deeper analysis revealed that LOF detected 55 unique outliers based on differences in local density, OCSVM

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Bouguessa, Mohamed. "A Mixture Model-Based Combination Approach for Outlier Detection." International Journal on Artificial Intelligence Tools 23, no. 04 (2014): 1460021. http://dx.doi.org/10.1142/s0218213014600215.

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In this paper, we propose an approach that combines different outlier detection algorithms in order to gain an improved effectiveness. To this end, we first estimate an outlier score vector for each data object. Each element of the estimated vectors corresponds to an outlier score produced by a specific outlier detection algorithm. We then use the multivariate beta mixture model to cluster the outlier score vectors into several components so that the component that corresponds to the outliers can be identified. A notable feature of the proposed approach is the automatic identification of outli

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Chen, Ping, Ling Dong, Wanyi Chen, and Jin-Guan Lin. "Outlier Detection in Adaptive Functional-Coefficient Autoregressive Models Based on Extreme Value Theory." Mathematical Problems in Engineering 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/910828.

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This paper proposes several test statistics to detect additive or innovative outliers in adaptive functional-coefficient autoregressive (AFAR) models based on extreme value theory and likelihood ratio tests. All the test statistics follow a tractable asymptotic Gumbel distribution. Also, we propose an asymptotic critical value on a fixed significance level and obtain an asymptoticp-value for testing, which is used to detect outliers in time series. Simulation studies indicate that the extreme value method for detecting outliers in AFAR models is effective both for AO and IO, for a lone outlier

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Fanny, Novika, Siswadi, and Bakhtiar Toni. "The Use of Biplot Analysis and Euclidean Distance with Procrustes Measure for Outliers Detection." International Journal of Engineering and Management Research 8, no. 1 (2018): 194–200. https://doi.org/10.5281/zenodo.3362826.

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Outlier is an object that has unique characteristics compared with other objects. Detection of outlier needs to be performed in order to avoid errors in decision-making related to data. Another reason for the detection of outlier is to determine the cause and the meaning of the difference from the outliers. Two methods for detection of outlier are Minimum Covariance Determinant (MCD) and Fast Minimum Covariance Determinant (FMCD). Unfortunately, MCD and FMCD require a longer iteration thus it is difficult to detect outlier in large data. In this work, we introduce alternative methods to detect

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Baba, Ali Mohammed, Habshah Midi, and Nur Haizum Abd Rahman. "Spatial Outlier Accommodation Using a Spatial Variance Shift Outlier Model." Mathematics 10, no. 17 (2022): 3182. http://dx.doi.org/10.3390/math10173182.

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Outlier detection has been a long-debated subject among researchers due to its effect on model fitting. Spatial outlier detection has received considerable attention in the recent past. On the other hand, outlier accommodation, particularly in spatial applications, retains vital information about the model. It is pertinent to develop a method that is capable of accommodating detected spatial outliers in a fashion that retains vital information in the spatial models. In this paper, we formulate the variance shift outlier model (SVSOM) in the spatial regression as a robust spatial model using re

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Sartore, Luca, Lu Chen, and Valbona Bejleri. "Empirical Inferences Under Bayesian Framework to Identify Cellwise Outliers." Stats 7, no. 4 (2024): 1244–58. http://dx.doi.org/10.3390/stats7040073.

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Outliers are typically identified using frequentist methods. The data are classified as “outliers” or “not outliers” based on a test statistic that measures the magnitude of the difference between a value and the majority part of the data. The threshold for a data value to be an outlier is typically defined by the user. However, a subjective choice of the threshold increases the uncertainty associated with outlier status for each data value. A cellwise outlier detection algorithm named FuzzyHRT is used to automate the editing process in repeated surveys. This algorithm uses Bienaymé–Chebyshev’

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Zhou, Shi Bo, and Wei Xiang Xu. "Local Outlier Detection Algorithm Based on Coefficient of Variation." Applied Mechanics and Materials 635-637 (September 2014): 1723–28. http://dx.doi.org/10.4028/www.scientific.net/amm.635-637.1723.

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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 indicat

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Rajalakshmi, P., and P. Geetha. "Detection of Outliers through Influence Function on Affinity." Mapana - Journal of Sciences 6, no. 2 (2007): 34–44. http://dx.doi.org/10.12723/mjs.11.2.

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Outliers are the atypical observations that lie at abnormal distances from the other observations in a random sample. Such outliers are often seen as contaminating the data. In general, the rejection of influential outliers improves the accuracy of the estimators and so the results with the identification of outliers have become the most important aspect in any data analysis. Outlier detection finds many applications in the areas such as data cleaning, fraud detection, network intrusion, pharmaceutical research and exploration in science data buses. The distance based outlier detection is the

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Wang, Lihui, Kangyi Zhi, Bin Li, and Yuexin Zhang. "Dynamically Adjusting Filter Gain Method for Suppressing GNSS Observation Outliers in Integrated Navigation." Journal of Navigation 71, no. 6 (2018): 1396–412. http://dx.doi.org/10.1017/s0373463318000334.

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Global Navigation Satellite Systems (GNSSs) are easily influenced by the external environment. Signals may be lost or become abnormal thereby causing outliers. The filter gain of the standard Kalman filter of a loosely coupled GNSS/inertial navigation system cannot change with the outliers of the GNSS, causing large deviations in the filtering results. In this paper, a method based on a χ2-test and a dynamically adjusting filter gain method are proposed to detect and separately to suppress GNSS observation outliers in integrated navigation. An indicator of an innovation vector is constructed,

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Steinbuss, Georg, and Klemens Böhm. "Benchmarking Unsupervised Outlier Detection with Realistic Synthetic Data." ACM Transactions on Knowledge Discovery from Data 15, no. 4 (2021): 1–20. http://dx.doi.org/10.1145/3441453.

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Benchmarking unsupervised outlier detection is difficult. Outliers are rare, and existing benchmark data contains outliers with various and unknown characteristics. Fully synthetic data usually consists of outliers and regular instances with clear characteristics and thus allows for a more meaningful evaluation of detection methods in principle. Nonetheless, there have only been few attempts to include synthetic data in benchmarks for outlier detection. This might be due to the imprecise notion of outliers or to the difficulty to arrive at a good coverage of different domains with synthetic da

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Thomas, Roy, and J. E. Judith. "A survey on outlier detection methods in data mining." International Journal of Engineering & Technology 7, no. 4 (2019): 6309–12. http://dx.doi.org/10.14419/ijet.v7i4.23153.

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Outliers are data objects whose characteristics differ from the mainstream characteristics of the data objects in a data set. Outlier detection plays a vital role in statistics as well as in data mining. Outlier detection effects to find out hidden and important information from large data sets. It has been a research field with diverse application areas for the past few decades. Outlier detection has been a topic of research in many fields like detecting malicious activity in cyber security, finding fake transactions in banking, detecting abnormality in medical data, identifying defects in in

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Vasuki, C. "OUTLIER DETECTION." International Scientific Journal of Engineering and Management 03, no. 03 (2024): 1–9. http://dx.doi.org/10.55041/isjem01499.

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To find observations that differ considerably from the bulk of the data points, outlier detection is an essential task in data analysis. To put it more simply, outliers are individual data points that stand out from the rest of the dataset. the Iris dataset, a machine learning benchmark, is used for outlier detection. The Rank SVM method, which is typically utilized for ranking jobs but has been modified for outlier identification, is used to find outliers. Standardizing the features pre-processes the dataset, which consists of measurements of iris flower sepal and petal diameters. The standar

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Hekimoglu, S., B. Erdogan, and R. C. Erenoglu. "A New Outlier Detection Method Considering Outliers As Model Errors." Experimental Techniques 39, no. 1 (2012): 57–68. http://dx.doi.org/10.1111/j.1747-1567.2012.00876.x.

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Ahmar, Ansari Saleh, Suryo Guritno, Abdurakhman, et al. "Modeling Data Containing Outliers using ARIMA Additive Outlier (ARIMA-AO)." Journal of Physics: Conference Series 954 (January 2018): 012010. http://dx.doi.org/10.1088/1742-6596/954/1/012010.

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Lalitha, S., and Nirpeksh Kumar. "Multiple outlier test for upper outliers in an exponential sample." Journal of Applied Statistics 39, no. 6 (2012): 1323–30. http://dx.doi.org/10.1080/02664763.2011.645158.

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Yang, Jiawei, Xu Tan, and Sylwan Rahardja. "MiPo: How to Detect Trajectory Outliers with Tabular Outlier Detectors." Remote Sensing 14, no. 21 (2022): 5394. http://dx.doi.org/10.3390/rs14215394.

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Trajectory outlier detection is one of the fundamental data mining techniques used to analyze the trajectory data of the Global Positioning System. A comprehensive literature review of trajectory outlier detectors published between 2000 and 2022 led to a conclusion that conventional trajectory outlier detectors suffered from drawbacks, either due to the detectors themselves or the pre-processing methods for the variable-length trajectory inputs utilized by detectors. To address these issues, we proposed a feature extraction method called middle polar coordinates (MiPo). MiPo extracted tabular

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Zhang, Yun, Bin Yang, Xi Zhao, Shiqian Wu, Bin Luo, and Liangpei Zhang. "Outlier Detection by Energy Minimization in Quantized Residual Preference Space for Geometric Model Fitting." Electronics 13, no. 11 (2024): 2101. http://dx.doi.org/10.3390/electronics13112101.

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Outliers significantly impact the accuracy of geometric model fitting. Previous approaches to handling outliers have involved threshold selection and scale estimation. However, many scale estimators assume that the inlier distribution follows a Gaussian model, which often does not accurately represent cases in geometric model fitting. Outliers, defined as points with large residuals to all true models, exhibit similar characteristics to high values in quantized residual preferences, thus causing outliers to cluster away from inliers in quantized residual preference space. In this paper, we lev

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Berki, S. E., and Nancy B. Schneier. "Frequency and Cost of Diagnosis-Related Group Outliers Among Newborns." Pediatrics 79, no. 6 (1987): 874–81. http://dx.doi.org/10.1542/peds.79.6.874.

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Analysis of outliers, as defined by the Health Core Financing Administration, among 47,776 newborns discharged from 33 short-term hospitals in Maryland in 1981 shows that the three prematurity diagnosis-related groups (DRGs) (386 to 388) represented only 5.3% of all discharges of newborns, but more than one fifth of all outliers and more than three fifths of outlier days of care for newborns. The disparity in charges for outliers and inliers (not exceeding the "trim point") is even more dramatic. Newborns with "extreme immaturity" (DRG 386) and "prematurity with major problems" (DRG 387) toget

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LI, SHUKAI, and WEE KEONG NG. "MAXIMUM VOLUME OUTLIER DETECTION AND ITS APPLICATIONS IN CREDIT RISK ANALYSIS." International Journal on Artificial Intelligence Tools 22, no. 05 (2013): 1360012. http://dx.doi.org/10.1142/s0218213013600129.

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Because of the scarcity and diversity of outliers, it is very difficult to design a robust outlier detector. In this paper, we first propose to use the maximum margin criterion to sift unknown outliers, which demonstrates superior performance. However, the resultant learning task is formulated as a Mixed Integer Programming (MIP) problem, which is computationally hard. Therefore, we alter the recently developed label generating technique, which efficiently solves a convex relaxation of the MIP problem of outlier detection. Specifically, we propose an effective procedure to find a largely viola

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Lee, Kyuman, and Eric N. Johnson. "Robust Outlier-Adaptive Filtering for Vision-Aided Inertial Navigation." Sensors 20, no. 7 (2020): 2036. http://dx.doi.org/10.3390/s20072036.

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With the advent of unmanned aerial vehicles (UAVs), a major area of interest in the research field of UAVs has been vision-aided inertial navigation systems (V-INS). In the front-end of V-INS, image processing extracts information about the surrounding environment and determines features or points of interest. With the extracted vision data and inertial measurement unit (IMU) dead reckoning, the most widely used algorithm for estimating vehicle and feature states in the back-end of V-INS is an extended Kalman filter (EKF). An important assumption of the EKF is Gaussian white noise. In fact, me

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Yang, Zi Rong, and Zhen Zeng. "Outlier Analysis in Large Sample and High Dimensional Data Based on Feature Weighting." Applied Mechanics and Materials 571-572 (June 2014): 650–57. http://dx.doi.org/10.4028/www.scientific.net/amm.571-572.650.

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The usual method of outlier analysis is mainly analyzing the outliers according to the Anomaly Index and Variable Contribution Measurement. But in the analysis of large samples of high-dimensional data, this method is difficult. Owing to this, this paper presents a method that weight value for outliers is introduced. The features of outliers are weighted by Analytic Hierarchy Process method. Through this method, the importance of each property of outlier for data mining’s target is rationed, namely the weight number of each property is calculated. And then the correlation values, which represe

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Rajalakshmi, S., and P. Madhubala. "Certain Investigation on Perpetualistic Fuzzy Outlier Data for Efficiency Evaluation of Centroid Stability with Cluster Boundary Fitness." Data Analytics and Artificial Intelligence 3, no. 2 (2023): 16–20. http://dx.doi.org/10.46632/daai/3/2/4.

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This paper aims to investigate certain factors that hide outliers in two dimensions such as boundary partitioning and space angular parameters. In this proposed algorithm, boundary representation of clusters, the data points that lie on the cluster boundary is stored geometrically as coordinate values such as i_bound (inliers) and o_bound(outliers). Outliers that present in dataset are investigated by boundary fitness over centroid stability. In this paper we focus to examine whether the data point lie on the boundary is treated as inliers or outliers. Several iterations are manipulated to fix

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Liu, Zhicheng, Yang Zhang, Ruihong Huang, Zhiwei Chen, Shaoxu Song, and Jianmin Wang. "EXPERIENCE: Algorithms and Case Study for Explaining Repairs with Uniform Profiles over IoT Data." Journal of Data and Information Quality 13, no. 3 (2021): 1–17. http://dx.doi.org/10.1145/3436239.

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IoT data with timestamps are often found with outliers, such as GPS trajectories or sensor readings. While existing systems mostly focus on detecting temporal outliers without explanations and repairs, a decision maker may be more interested in the cause of the outlier appearance such that subsequent actions would be taken, e.g., cleaning unreliable readings or repairing broken devices or adopting a strategy for data repairs. Such outlier detection, explanation, and repairs are expected to be performed in either offline (batch) or online modes (over streaming IoT data with timestamps). In this

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Ginni, Girish Reddy, and Dr Srinivasa L. Chakravarthy. "Efficient Outlier Detection in High-Dimensional Data Using Unsupervised Machine Learning." Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications 15, no. 4 (2024): 192–212. https://doi.org/10.58346/jowua.2024.i4.013.

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A fundamental concept in data mining and ML is outlier detection. Outlier identification and clustering often work together, as identifying outliers can lead to better clustering. Most current research projects have focused primarily on outlier identification and clustering as separate aspects, but their close relationship needs to be explored. By considering this relationship, we can improve cluster quality while detecting outliers, providing dual benefits. We have proposed an unsupervised ML framework for efficiently detecting outliers in high-dimensional datasets. An objective function has

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Yuliatin, Umi. "DETEKSI OUTLIERS DAN ANALISIS INTERVENSI DALAM MODEL ARMA." MAp (Mathematics and Applications) Journal 4, no. 1 (2022): 76–84. http://dx.doi.org/10.15548/map.v4i1.4279.

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Adanya kehadiran outliers dalam analisisi runtun waktu mengaburkan estimasi parameter model yang diberikan. Selain itu outlier juga memberi dampak besaran eror yang lebih tinggi. Dalam analisis time series Additive outliers (AO) dan innovational outliers (IO) diperkenalkan sebagai usaha dalam memodelkan outliers. Usaha ini diberikan untuk menangani obserbasi yang tidak mengharmoniskan pola data sehingga membantu untuk dibentuknya model runtun waktu yang sehat terutama dalam proses ARMA. Estimator linier square error (LSE) digunakan untuk mengestimasi besarnya penyimpangan dari model dasarnya.

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