Outliers

Outlier (or anomaly) detection is an important problem for many domains, including fraud detection, risk analysis, network intrusion and medical diagnosis, and the discovery of significant outliers is becoming an integral aspect of data mining. This paper presents CURIO, a novel algorithm that uses quantisation and implied distance metrics to provide a fast algorithm that is linear for the number of objects and only requires two sequential scans of disk resident datasets. CURIO includes a novel direct quantisation technique and the ...

Elastic full waveform inversion of seismic reflection data represents a data-driven form of analysis leading to quantification of sub-surface parameters in depth. In previous studies attention has been given to P-wave data recorded in the marine environment, using either acoustic or elastic inversion schemes. In this paper we exploit both P-waves and mode-converted S-waves in the marine environment in the inversion for both P- and S-wave velocities by using wide-angle, multi-component, ocean-bottom cable seismic data. An elastic waveform inversion scheme operating in the time domain was used, allowing accurate modelling of the full wavefield, including the elastic amplitude variation with offset response of reflected arrivals and mode-converted events. A series of one- and two-dimensional synthetic examples are presented, demonstrating the ability to invert for and thereby to quantify both P- and S-wave velocities for different velocity models. In particular, for more realistic low velocity models, including a typically soft seabed, an effective strategy for inversion is proposed to exploit both P- and mode-converted PS-waves. Whilst P-wave events are exploited for inversion for P-wave velocity, examples show the contribution of both P- and PS-waves to the successful recovery of S-wave velocity.

Sales forecasting became crucial for industries in past decades with rapid globalization, widespread adoption of information technology towards e-business, understanding market fluctuations, meeting business plans, and avoiding loss of sales. This research precisely predicts the automotive industry sales using a bag of multiple machine learning and time series algorithms coupled with historical sales and auxiliary features. Three-year historical sales data (from 2017 till 2020) were used for the model building or training, and one-year (2020-2021) predictions were computed for 900 unique SKU's (stock-keeping units). In the present study, the SKU is a combination of sales office, core business field, and material customer group. Various data cleaning and exploratory data analysis algorithms were implemented over raw datasets before use for modeling. Mean absolute percentage error (mape) were estimated for individual predictions from time series and machine learning models. The best model was selected for unique SKU's as per the most negligible mape value.

The book provides a thorough, up-to-date description of robust methods that are aimed at dealing with non-normality, heteroscedasticity, outliers and curvature. The primary focus is on the practical applications of modern robust methods, which can greatly enhance our chances of detecting true differences among groups and true associatons amon variables. Includes latest improvements in ANOVA, regression, rank-based methods. Describes and illustrates how to apply robust methods using R.

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Before we conduct the actual statistical tests, we need to screen our data for any irregularity. Usually, we check for: (a) if data have been entered correctly, such as out-of-range values. It may be caused by human error in data entry (eg: entering " 22 " when it is supposed to be " 2 " for likert scale item) (b) for other kind of outliers (multivariate). Outliers are suspiciously larger or smaller observation (data) than the majority of the observations. (c) for missing values. Is it because of you miss out entering some data or your participant did not provide a response for some questions. (d) for checking assumptions before conducting tests (eg. Normality)

Abstrak-Data mining adalah analisis atau pengamatan terhadap kumpulan data yang besar dengan tujuan untuk menemukan hubungan tak terduga dan untuk meringkas data dengan cara yang lebih mudah dimengerti dan bermanfaat bagi pemilik data. Data mining merupakan proses inti dalam Knowledge Discovery in Database (KDD). Metode data mining digunakan untuk menganalisis data pembayaran kredit peminjam pembayaran kredit. Berdasarkan pola pembayaran kredit peminjam yang dihasilkan, dapat dilihat parameter-parameter kredit yang memiliki keterkaitan dan paling berpengaruh terhadap pembayaran angsuran kredit. Kata kunci-data mining, outlier, multikolonieritas, Anova I. PENDAHULUAN Dalam dunia perbankan, bank bertindak sebagai kreditur, di mana bank memberikan bantuan kepada nasabah yang membutuhkan pinjaman dengan memberikan kredit pinjaman kepada nasabahnya. Bank memberikan kredit pinjaman kepada nasabah yang dianggap mampu melunasi kredit setiap bulan yang besarnya sesuai dengan perjanjian antara kedua belah pihak. Bank memberikan kemudahan bagi nasabah yang ingin meminjam uang, yaitu dengan membuat program kredit pinjaman sebagai salah satu solusi keuangan. Namun demikian, terdapat sejumlah permasalahan yang muncul dari program kredit pinjaman. Salah satu permasalahan yang sering muncul adalah adanya nasabah yang telat membayar angsuran. Penyebab yang biasa terjadi adalah adanya nasabah yang sebenarnya telah memenuhi kualifikasi peminjaman kredit tetapi nasabah tersebut memiliki potensi yang tinggi untuk terlambat membayar kredit. Misalnya orang yang meminjam tersebut memiliki banyak tanggungan, memiliki angsuran lain dan sebagainya. Analisis terhadap data kredit bank diperlukan dengan tujuan untuk meminimalisasi resiko nasabah yang terlambat membayar kredit. Analisis yang akan dilakukan adalah analisis menggunakan metode data mining untuk melihat parameter kredit dari nasabah yang melakukan pinjaman, dalam hal ini data yang akan digunakan adalah data German Credit. Hasil dari analisis ini adalah parameter kredit. Berdasarkan parameter kredit yang dihasilkan, dapat dibuat suatu penilaian terhadap status kredit pada data German Credit yaitu terlambat bayar (overdue) atau membayar tepat waktu. Makalah ini disusun dengan urutan topik bahasan pendahuluan yang berisi latar belakang, tinjauan pustaka, metode penelitian, pembahasan hasil penelitian, dan penutup berupa kesimpulan dan saran, disertai dengan daftar pustaka. II. TINJAUAN PUSTAKA Data mining adalah analisis pengamatan kumpulan data (yang besar) untuk menemukan hubungan tak terduga dan untuk meringkas data dengan cara baru yang mudah dimengerti dan bermanfaat bagi pemilik data [1]. Hubungan dan ringkasan yang diperoleh melalui pelatihan data mining sering disebut sebagai model atau pola. Data mining digunakan untuk mengekstraksi informasi prediktif tersembunyi di dalam database [7]. Data mining mengacu pada analisis data yang kuantitasnya besar dan disimpan di komputer [5]. Dalam dunia perbankan, data mining dapat digunakan untuk mendeteksi pola kecurangan bertransaksi, memodelkan pola dan perilaku nasabah, mendeteksi kejadian yang tidak wajar, dan menemukan pengetahuan. Dalam penerapan data mining pada data berskala besar diperlukan metodologi sistematis untuk menganalisis dan mempersiapkan data. Metodologi sistematis juga dibutuhkan untuk melakukan interpretasi sehingga dapat menghasilkan keputusan yang bermanfaat. Data mining merupakan bagian dari Knowledge Discovery in Databases (KDD). Knowledge Discovery in Databases atau biasa disingkat dengan KDD berisi serangkaian proses-proses yang harus dilakukan sebelum dan sesudah menganalisis dengan metode data mining. Langkah-langkah yang dilakukan dalam

By comparing historical data of trading like daily Open, High, Low, Close, Volume, Number of Trades, Turnover, Delivery percentage etc. of a particular stock with its Peer Group companies and Non Peer Group companies stocks for a particular period, we can find some unusual observations which are also known as outliers. In this paper we have tried to detect the observations, which are very different from the other observations using a Data Mining Technique for Outlier Detection-"Multiple Linear Regression Analysis".

This book is a two-semeser, graduate-level introduction to statistics. Classic methods are covered including simple explanations of when and why they  can be unsatisfactory. This is followed by current results on how modern robust methods deal with known problems. Many illustrations indicate how to apply both classic and modern robust methods using R.

This second edition contains major changes and additions. All of the chapters have been updated; for some there are only minor changes, but for others there are major changes. Many new R functions have been added that reflect  recent advances.  Briefly, they deal with new methods for comparing groups and studying associations. For example, it is now possible to compare a collection of quantiles in a manner that controls the probability of one or more Type I errors,  even when there are tied values. This new approach can provide a deeper understanding of how groups compare in contrast to methods  that focus on a single measure of location. Some new multiple comparison procedures are covered as well as some new techniques related to testing global hypotheses. New methods for comparing regression lines are covered and several new and improved methods for dealing with ANCOVA are described.  There is even a new method for determining which independent variables are most important in a regression model. There are many ways of estimating which variables are most important. What is new here is the ability to determine the strength of the empirical evidence that the most important variables are correctly
identified.

The aim of this research is to come up with a solution that can be used in our institutions of learning to report life threatening incidents timely and appropriately. The motivation of the research is the unfortunate terror attack that occurred at Garissa university in Kenya on April 2, 2015 leaving more than 147 dead.
The main research  method are literature search, personal interviews with heads of Learning  institutions and county security commissioners.
The anticipated direct benefits are to timely report life threatening incidents to relevant authority,to help minimize loss of life, to advice on appropriate course of action to be taken by rescue team and hostage, to create secure learning environment for students. Some indirect benefits include job creation on big data analysis, data mining as there is need for further analysis on the data collected by the surveillance system over time be used by security intelligence to aid curb future security lapse

ARTICLE INFO ABSTRACT A look at the psychology literature reveals that researchers still seem to encounter difficulties in coping with multivariate outliers. Multivariate outliers can severely distort the estimation of population parameters. Detecting multivariate outliers is mainly disregarded or done by using the basic Mahalanobis distance. However, that indicator uses the multivariate sample mean and covariance matrix that are particularly sensitive to outliers. Hence, this method is problematic. We highlight the disadvantages of the basic Mahalanobis distance and argue instead in favor of a robust Mahalanobis distance. In particular, we present a variant based on the Minimum Co-variance Determinant, a more robust procedure that is easy to implement. Using Monte Carlo simulations of bi-variate sample distributions varying in size (ns = 20, 100, 500) and population correlation coefficient (ρ = .10, .30, .50), we demonstrate the detrimental impact of outliers on parameter estimation and show the superiority of the MCD over the Mahalanobis distance. We also make recommendations for deciding whether to include vs. exclude outliers. Finally, we provide the procedures for calculating this indicator in R and SPSS software.

In a linear regression model, the ordinary least squares (OLS) method is considered the best method to estimate the regression parameters if the assumptions are met. However, if the data does not satisfy the underlying assumptions, the results will be misleading. The violation for the assumption of constant variance in the least squares regression is caused by the presence of outliers and heteroscedasticity in the data. This assumption of constant variance (homoscedasticity) is very important in linear regression in which the least squares estimators enjoy the property of minimum variance. Therefor e robust regression method is required to handle the problem of outlier in the data. However, this research will use the weighted least square techniques to estimate the parameter of regression coefficients when the assumption of error variance is violated in the data. Estimation of WLS is the same as carrying out the OLS in a transformed variables procedure. The WLS can easily be affecte...

Raw data collected through surveys, experiments, coding of textual artifacts or other quantitative means may not meet the assumptions upon which statistical analyses rely. The presence of univariate or multivariate outliers, skewness or kurtosis in a distribution, and heteroscedasticity or multicollinearity among variables may compromise data analysis. Scholars have identified a variety of techniques to discern such problems and address them.

— Thermal/power cycles are widely acknowledged methods to accelerate the package related failures. Many studies have focused on one particular aging precursor at a time and continuously monitored it using custom-built circuits. Due to the difficulties in taking sensitive measurements, the reported findings are more on the quantities requiring less sensitive measurements. In this paper, two custom-designed test beds are used to age a number of power MOSFETs and IGBTs. An automated curve tracer is utilized to capture parametric variations in I-V curves, parasitic capacitances and gate charges at certain time intervals. The results suggest that the only viable aging precursors are the on-state voltage drop / on-state resistance, body diode voltage drop, parasitic capacitances and gate threshold voltage for die attach solder and gate-oxide degradation mechanisms. Based on the experimental results, gate threshold voltage variation is empirically modeled to estimate the remaining useful lifetime of the switches experiencing gate oxide degradation. The model parameters are found by the least-squares method applied to inliers determined by the RANSAC outlier removal algorithm.

Pemodelan menggunakan analisis regresi linier berganda memiliki tindak lanjut setelah full model terbentuk. Tindak lanjut dilakukan untuk mendapatkan hasil evaluasi yang terbaik untuk model. Pemodelan menggunakan analisis regresi berganda menghasilkan nilai Residual Standard Error dan Koefisien Determinasi R2 yang dapat dijadikan evaluasi model yang sudah terbentuk (James, 2013). Metode tindak lanjut setelah full model terbentuk adalah dengan penerapan model dengan deteksi outliers dan transformasi pada variabel respon. Pada studi kasus menggunakan data PDAM Provinsi Jawa Barat, Jawa Timur, D.I Yogyakarta dan Jawa Timur pada Data Rekapitulasi Kinerja PDAM di Indonesia Tahun 2017. Berdasarkan 4 metode yang sudah digunakan, maka model yang memiliki Residual Standard Error terendah dan nilai koefisien determinasi tertinggi adalah Model dengan Metode Regresi berganda dengan menghilangkan observasi yang dianggap Outliers dengan memperhatikan nilai Externally Studentized Residuals.

Hacia fines del siglo XX el estudio de las estructuras en red de la World Wide Web y la Internet demostró en forma dramática que las diversas distribuciones estadísticas que les eran propias no respondían al modelo de las distribuciones normales sino que se ajus­ta­ban a leyes de potencia (Barabási 2002; Reynoso 2011). Una ley de potencia (en lo sucesivo LP), similar a las viejas leyes de Pareto y de Zipf, difiere de las distribuciones gaussianas y afines tanto como es posible que dos cosas difieran, cualitativa y cuantitativamente. Al ser la LP una distribución en la que no es ni útil ni posible definir medias o promedios, en la que no existe un valor que pueda reputarse “normal” (inter­medio más o menos exacto entre los valores extremos) y a la que no puede llegarse me­diante operaciones de muestreo, gran parte de las esta­dís­ticas convencionales no le son aplicables.

El problema que deseo tratar aquí tiene que ver con el hecho de que si bien el descu­bri­miento de la vigencia de la LP en la mayor parte de los fenómenos y procesos sociales y culturales ha sido plenamente reconocido, las ciencias humanas en general y el análisis de redes sociales en particular siguen adelante su negocio sin tomar cabalmente en cuenta el nue­vo estado de cosas.

No se han cambiado correspondientemente ni los diseños investi­ga­ti­vos ni las herramientas de software, que siguen aplicándose como si la distribución nor­mal siguiera siendo, como se quiso que fuera hasta hace poco, la madre de todas las leyes. Los métodos implicados en ella, capítulo fundamental de un aleatorismo envolvente que se obstina en presentar una pregunta mal planteada como si fuera una respuesta inteligente, siguen osbtaculizando el camino hacia métodos de modelado matemático que son a todas luces más instrumentales para empezar a comprender las complejidades involucradas.

Este libro finalmente publicado (uno de los ensayos de más alta complejidad entre los que he escrito) constituye una obra abierta que ha estado en gestación durante años y que todavía no me decido a dar por concluido. Si el texto parece referirse a un asunto enrevesadamente específico, pienso que eso es sólo un efecto secundario, un factor periférico: no es tanto la NHST lo que está aquí en juego, sino el sentido y el alcance del modelado científico en general cuando lo que toca afrontar son objetos complejos, como inevitablemente lo son todos los que cabe postular en nuestras disciplinas.

Aunque la Corte Suprema de los Estados Unidos sabe que la prueba de significancia de lo hipótesis nula es insostenible (pregunten si no a Laboratorios Merck) y aunque la Asociación Americana de Psicología aconseja ahora prescindir de las pruebas estadísticas basadas en distribuciones gaussianas, muchos de los arqueólogos que conozco siguen creyendo que las distribuciones normales existen en la vida real mientras los antropólogos continúan pensando que el juicio de Clifford Geertz que aconseja posicionarse a mitad de camino entre los errores de tipo 1 y los del tipo 2 constituye una muestra impar de sabiduría conceptual.

This work represents the research material, classifying and systematiz-ing commonly used outliers detecting procedures for their main character-istics: population or sample size, the nature of its distribution, work with the output data and procedures essential characteristics. For the practical use of detecting outliers (extreme values) procedures and further applica-tion of research results in practice, the author has proposed an algorithm of the statistical analysis of data containing outliers. The review of foreign lit-erature on decision-making practice on the basis of data containing outliers is also presented in the work. The statistical report for the manager pro-posed to be the main special statistical instrument providing more effective business communications between managers and specialists and allows the statistical analysis become a powerful and effective tool for managers to make informed decisions.

Two semester book that covers standard topics plus many advances and insights not typically covered at this level. Describes and illustrates how to apply conventional methods using R plus many modern robust and nonparamtric methods developed in recent years.

Abstrak-Data mining adalah analisis atau pengamatan terhadap kumpulan data yang besar dengan tujuan untuk menemukan hubungan tak terduga dan untuk meringkas data dengan cara yang lebih mudah dimengerti dan bermanfaat bagi pemilik data. Data mining merupakan proses inti dalam Knowledge Discovery in Database (KDD). Metode data mining digunakan untuk menganalisis data pembayaran kredit peminjam pembayaran kredit. Berdasarkan pola pembayaran kredit peminjam yang dihasilkan, dapat dilihat parameter-parameter kredit yang memiliki keterkaitan dan paling berpengaruh terhadap pembayaran angsuran kredit. Kata kunci-data mining, outlier, multikolonieritas, Anova I. PENDAHULUAN Dalam dunia perbankan, bank bertindak sebagai kreditur, di mana bank memberikan bantuan kepada nasabah yang membutuhkan pinjaman dengan memberikan kredit pinjaman kepada nasabahnya. Bank memberikan kredit pinjaman kepada nasabah yang dianggap mampu melunasi kredit setiap bulan yang besarnya sesuai dengan perjanjian antar...

Common significance tests carried out using statistical software packages usually return to the user the probability p of type I error as the result. Based on p and the preset confidence level the user will decide on the acceptance or the rejection of the associated null hypothesis. Dixon's test (Q-test) is commonly used for the detection of an outlier within a set of N observations (typically: N=3-12). Q-test can only be applied by comparing the experimental value of the statistic Q with tabulated critical Q-values corresponding to some standard values of p. Hence, for a given value of Q and a number of observations, N, the user knows only the range and not the value of the associated probability p of type I error (erroneous rejection). This is due to the lack of explicit expressions of the form p=F(Q,N). In this work, a simple stochastic (Monte Carlo) approach is presented for the estimation of p corresponding to a given experimental value of Q and size N of the data set. In a...

Although the linear mixed model can be viewed as a direct extension of multiple regression, it is not obvious how to generalize the standard diagnostic tools such as residual analysis and detection of leverage points and outliers, which are available in the linear regression situation. A unified approach to residuals, leverages and outliers in the linear mixed model is developed. Formal and informal procedures are proposed to display the general features of residuals and leverages in order to detect outliers and high-leverage points in the linear mixed models. The relationship between the best linear unbiased predictor (BLUP) and residuals is established. Some properties of BLUPs are formulated and their use in detecting outlying observations are investigated.

Existing studies in data mining focus on Outlier detection on data with single clustering algorithm mostly. There are lots of Clustering methods available in data mining. The values or objects that are similar to each other are organized in group it’s called cluster and the values or objects that do not comply with the model or general behavior of the data these data objects called outliers. Outliers detect by clustering. Many Algorithms have been developed for clustering. Where partitional and Hierarchical Clustering is the two well known methods for clustering. In comparison of Hierarchical and Partitional Clustering Majority of the Hierarchical algorithms are very computationally, complex and consume high memory space. Whereas majority of Partitional clustering algorithm have required a linear time with better effectiveness. The clustering quality is not as Better as that a Hierarchical clustering algorithm. Hierarchical and Partitional Clustering algorithm have advantage over each other so in our proposed algorithm we integrate the Partitional Algorithm K-Modes because of Categorial Dataset and Hierarchical Clustering Algorithm CURE because of Large dataset, robust to outliers and identified cluster having non-spherical shape. And we plan to implement that algorithm in MapReduce Framework so the execution time of the algorithm is improve.