Homophily Outlier Detection in Non-IID Categorical Data

Most of existing outlier detection methods assume that the outlier factors (i.e., outlierness scoring measures) of data entities (e.g., feature values and data objects) are Independent and Identically Distributed (IID). This assumption does not hold in real-world applications where the outlierness of different entities is dependent on each other and/or taken from different probability distributions (non-IID). This may lead to the failure of detecting important outliers that are too subtle to be identified without considering the non-IID nature. The issue is even intensified in more challenging contexts, e.g., high-dimensional data with many noisy features. This work introduces a novel outlier detection framework and its two instances to identify outliers in categorical data by capturing non-IID outlier factors. Our approach first defines and incorporates distribution-sensitive outlier factors and their interdependence into a value-value graph-based representation. It then models an outlierness propagation process in the value graph to learn the outlierness of feature values. The learned value outlierness allows for either direct outlier detection or outlying feature selection. The graph representation and mining approach is employed here to well capture the rich non-IID characteristics. Our empirical results on 15 real-world data sets with different levels of data complexities show that (i) the proposed outlier detection methods significantly outperform five state-of-the-art methods at the 95%/99% confidence level, achieving 10%-28% AUC improvement on the 10 most complex data sets; and (ii) the proposed feature selection methods significantly outperform three competing methods in enabling subsequent outlier detection of two different existing detectors.Comment: To appear in Data Ming and Knowledge Discovery Journa

CURE: Flexible Categorical Data Representation by Hierarchical Coupling Learning

© 1989-2012 IEEE. The representation of categorical data with hierarchical value coupling relationships (i.e., various value-to-value cluster interactions) is very critical yet challenging for capturing complex data characteristics in learning tasks. This paper proposes a novel and flexible coupled unsupervised categorical data representation (CURE) framework, which not only captures the hierarchical couplings but is also flexible enough to be instantiated for contrastive learning tasks. CURE first learns the value clusters of different granularities based on multiple value coupling functions and then learns the value representation from the couplings between the obtained value clusters. With two complementary value coupling functions, CURE is instantiated into two models: coupled data embedding (CDE) for clustering and coupled outlier scoring of high-dimensional data (COSH) for outlier detection. These show that CURE is flexible for value clustering and coupling learning between value clusters for different learning tasks. CDE embeds categorical data into a new space in which features are independent and semantics are rich. COSH represents data w.r.t. an outlying vector to capture complex outlying behaviors of objects in high-dimensional data. Substantial experiments show that CDE significantly outperforms three popular unsupervised encoding methods and three state-of-the-art similarity measures, and COSH performs significantly better than five state-of-the-art outlier detection methods on high-dimensional data. CDE and COSH are scalable and stable, linear to data size and quadratic to the number of features, and are insensitive to their parameters

Advances in knowledge discovery and data mining Part II

19th Pacific-Asia Conference, PAKDD 2015, Ho Chi Minh City, Vietnam, May 19-22, 2015, Proceedings, Part II</p

Statistical learning in complex and temporal data: distances, two-sample testing, clustering, classification and Big Data

Programa Oficial de Doutoramento en Estatística e Investigación Operativa. 555V01[Resumo] Esta tesis trata sobre aprendizaxe estatístico en obxetos complexos, con énfase en series temporais. O problema abórdase introducindo coñecemento sobre o dominio do fenómeno subxacente, mediante distancias e características. Proponse un contraste de dúas mostras basado en distancias e estúdase o seu funcionamento nun gran abanico de escenarios. As distancias para clasificación e clustering de series temporais acadan un incremento da potencia estatística cando se aplican a contrastes de dúas mostras. O noso test compárase de xeito favorable con outros métodos gracias á súa flexibilidade ante diferentes alternativas. Defínese unha nova distancia entre series temporais mediante un xeito innovador de comparar as distribucións retardadas das series. Esta distancia herda o bo funcionamento empírico doutros métodos pero elimina algunhas das súas limitacións. Proponse un método de predicción baseada en características das series. O método combina diferentes algoritmos estándar de predicción mediante unha suma ponderada. Os pesos desta suma veñen dun modelo que se axusta a un conxunto de entrenamento de gran tamaño. Propónse un método de clasificación distribuida, baseado en comparar, mediante unha distancia, as funcións de distribución empíricas do conxuto de proba común e as dos datos que recibe cada nodo de cómputo.[Resumen] Esta tesis trata sobre aprendizaje estadístico en objetos complejos, con énfasis en series temporales. El problema se aborda introduciendo conocimiento del dominio del fenómeno subyacente, mediante distancias y características. Se propone un test de dos muestras basado en distancias y se estudia su funcionamiento en un gran abanico de escenarios. La distancias para clasificación y clustering de series temporales consiguen un incremento de la potencia estadística cuando se aplican al tests de dos muestras. Nuestro test se compara favorablemente con otros métodos gracias a su flexibilidad antes diferentes alternativas. Se define una nueva distancia entre series temporales mediante una manera innovadora de comparar las distribuciones retardadas de la series. Esta distancia hereda el buen funcionamiento empírico de otros métodos pero elimina algunas de sus limitaciones. Se propone un método de predicción basado en características de las series. El método combina diferentes algoritmos estándar de predicción mediante una suma ponderada. Los pesos de esta suma salen de un modelo que se ajusta a un conjunto de entrenamiento de gran tamaño. Se propone un método de clasificación distribuida, basado en comparar, mediante una distancia, las funciones de distribución empírica del conjuto de prueba común y las de los datos que recibe cada nodo de cómputo.[Abstract] This thesis deals with the problem of statistical learning in complex objects, with emphasis on time series data. The problem is approached by facilitating the introduction of domain knoweldge of the underlying phenomena by means of distances and features. A distance-based two sample test is proposed, and its performance is studied under a wide range of scenarios. Distances for time series classification and clustering are also shown to increase statistical power when applied to two-sample testing. Our test compares favorably to other methods regarding its flexibility against different alternatives. A new distance for time series is defined by considering an innovative way of comparing lagged distributions of the series. This distance inherits the good empirical performance of existing methods while removing some of their limitations. A forecast method based on times series features is proposed. The method works by combining individual standard forecasting algorithms using a weighted average. These weights come from a learning model fitted on a large training set. A distributed classification algorithm is proposed, based on comparing, using a distance, the empirical distribution functions between the dataset that each computing node receives and the test set

Joint ranking and clustering based on Markov Chain transition probabilities learned from data

The focus of this thesis is to develop a Markov Chain based framework for joint ranking and clustering of a dataset without the need for critical user-defined hyper-parameters. Joint ranking and clustering may be useful in several respects, and may give additional insight for the data analyst, as opposed to the traditional separate ranking and clustering procedures. By coupling Markov chain theory with recent advances in kernel methods using the so-called probabilistic cluster kernel, we are able to learn the transition probabilities from the inherent structures in the data in a near parameter-free approach. The theory developed in this thesis is applied to several real world datasets of different types with promising results

ML-based data-entry automation and data anomaly detection to support data quality assurance

Data playsacentralroleinmodernsoftwaresystems,whichare very oftenpoweredbymachinelearning(ML)andusedincriticaldo- mains ofourdailylives,suchasfinance,health,andtransportation. However,theeffectivenessofML-intensivesoftwareapplicationshighly depends onthequalityofthedata.Dataqualityisaffectedbydata anomalies; dataentryerrorsareoneofthemainsourcesofanomalies. The goalofthisthesisistodevelopapproachestoensuredataquality by preventingdataentryerrorsduringtheform-fillingprocessandby checking theofflinedatasavedindatabases. The maincontributionsofthisthesisare: 1. LAFF, anapproachtoautomaticallysuggestpossiblevaluesofcat- egorical fieldsindataentryforms. 2. LACQUER, anapproachtoautomaticallyrelaxthecompleteness requirementofdataentryformsbydecidingwhenafieldshould be optionalbasedonthefilledfieldsandhistoricalinputinstances. 3. LAFF-AD, anapproachtoautomaticallydetectdataanomaliesin categorical columnsinofflinedatasets. LAFF andLACQUERfocusmainlyonpreventingdataentryerrors during theform-fillingprocess.Bothapproachescanbeintegratedinto data entryapplicationsasefficientandeffectivestrategiestoassistthe user duringtheform-fillingprocess.LAFF-ADcanbeusedofflineon existing suspiciousdatatoeffectivelydetectanomaliesincategorical data. In addition,weperformedanextensiveevaluationofthethreeap- proaches,assessingtheireffectivenessandefficiency,usingreal-world datasets

Structured Prediction on Dirty Datasets

Many errors cannot be detected or repaired without taking into account the underlying structure and dependencies in the dataset. One way of modeling the structure of the data is graphical models. Graphical models combine probability theory and graph theory in order to address one of the key objectives in designing and fitting probabilistic models, which is to capture dependencies among relevant random variables. Structure representation helps to understand the side effect of the errors or it reveals correct interrelationships between data points. Hence, principled representation of structure in prediction and cleaning tasks of dirty data is essential for the quality of downstream analytical results. Existing structured prediction research considers limited structures and configurations, with little attention to the performance limitations and how well the problem can be solved in more general settings where the structure is complex and rich. In this dissertation, I present the following thesis: By leveraging the underlying dependency and structure in machine learning models, we can effectively detect and clean errors via pragmatic structured predictions techniques. To highlight the main contributions: I investigate prediction algorithms and systems on dirty data with a more realistic structure and dependencies to help deploy this type of learning in more pragmatic settings. Specifically, We introduce a few-shot learning framework for error detection that uses structure-based features of data such as denial constraints violations and Bayesian network as co-occurrence feature. I have studied the problem of recovering the latent ground truth labeling of a structured instance. Then, I consider the problem of mining integrity constraints from data and specifically using the sampling methods for extracting approximate denial constraints. Finally, I have introduced an ML framework that uses solitary and structured data features to solve the problem of record fusion

CLADAG 2021 BOOK OF ABSTRACTS AND SHORT PAPERS

The book collects the short papers presented at the 13th Scientific Meeting of the Classification and Data Analysis Group (CLADAG) of the Italian Statistical Society (SIS). The meeting has been organized by the Department of Statistics, Computer Science and Applications of the University of Florence, under the auspices of the Italian Statistical Society and the International Federation of Classification Societies (IFCS). CLADAG is a member of the IFCS, a federation of national, regional, and linguistically-based classification societies. It is a non-profit, non-political scientific organization, whose aims are to further classification research