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arXiv 2019年

作者： Tatti, Nikolaj Vreeken, Jilles Advanced Database Research and Modelling Department of Mathematics and Computer Science University of Antwerp Antwerp Belgium

Deciding whether the results of two different mining algorithms provide significantly different information is an important, yet understudied, open problem in exploratory data mining. Whether the goal is to select the most informative result for analysis, or to decide which mining approach will most likely provide the most novel insight, it is essential that we can tell how different the information is that different results by possibly different methods provide. In this paper we take a first step towards comparing exploratory data mining results on binary data. We propose to meaningfully convert results into sets of noisy tiles, and compare between these sets by Maximum Entropy modelling and Kullback-Leibler divergence, well-founded notions from Information Theory. We so construct a measure that is highly flexible, and allows us to naturally include background knowledge, such that differences in results can be measured from the perspective of what a user already knows. Furthermore, adding to its interpretability, it coincides with Jaccard dissimilarity when we only consider exact tiles. Our approach provides a means to study and tell differences between results of different exploratory data mining methods. As an application, we show that our measure can also be used to identify which parts of results best redescribe other results. Furthermore, we study its use for iterative data mining, where one iteratively wants to find that result that will provide maximal novel information. Experimental evaluation shows our measure gives meaningful results, correctly identifies methods that are similar in nature, automatically provides sound redescriptions of results, and is highly applicable for iterative data mining. Copyright © 2019, The Authors. All rights reserved.

关键词： Data mining

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Author Correction: Multidisciplinary study of the secondary immune response in grandparents re-exposed to chickenpox

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Scientific reports 2018年第1期8卷 10824页

作者： B Ogunjimi J Van den Bergh P Meysman S Heynderickx K Bergs H Jansens E Leuridan A Vorsters H Goossens K Laukens N Cools Viggo Van Tendeloo N Hens P Van Damme Evelien Smits Ph Beutels Centre for Health Economics Research & Modeling Infectious Diseases (CHERMID) Vaccine & Infectious Disease Institute (VAXINFECTIO) University of Antwerp Antwerp Belgium. benson.ogunjimi@uantwerp.be. Interuniversity Institute for Biostatistics and Statistical Bioinformatics Hasselt University Hasselt Belgium. benson.ogunjimi@uantwerp.be. Department of Pediatrics Antwerp University Hospital Edegem Belgium. benson.ogunjimi@uantwerp.be. Laboratory of Experimental Hematology (LEH) Vaccine & Infectious Disease Institute (VAXINFECTIO) University of Antwerp Antwerp Belgium. benson.ogunjimi@uantwerp.be. Department of Pediatrics Ghent University Hospital Ghent Belgium. benson.ogunjimi@uantwerp.be. Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS) Antwerp Belgium. benson.ogunjimi@uantwerp.be. Laboratory of Experimental Hematology (LEH) Vaccine & Infectious Disease Institute (VAXINFECTIO) University of Antwerp Antwerp Belgium. Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS) Antwerp Belgium. Advanced Database Research and Modelling (ADReM) Department of Mathematics and Computer Science University of Antwerp Antwerp Belgium. Biomedical Informatics Research Network Antwerp (BIOMINA) University of Antwerp/Antwerp University Hospital Edegem Belgium. Center for Cell Therapy and Regenerative Medicine Antwerp University Hospital Edegem Belgium. Department of Laboratory Medicine Antwerp University Hospital Edegem Belgium. Centre for the Evaluation of Vaccination (CEV) Vaccine & Infectious Disease Institute (VAXINFECTIO) University of Antwerp Antwerp Belgium. Laboratory of Medical Microbiology Vaccine & Infectious Disease Institute (VAXINFECTIO) University of Antwerp Antwerp Belgium. Centre for Health Economics Research & Modeling Infectious Diseases (CHERMID) Vaccine & Infectious Disease Institute (VAXINFECTIO) University of Antwerp Antwerp Belgium. Interuniversity Institute for Biostatistics and Statistical Bio

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper.

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Maximum entropy models for iteratively identifying subjectively interesting structure in real-valued data

Maximum entropy models for iteratively identifying subjectiv...

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European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in databases, ECML PKDD 2013

作者： Kontonasios, Kleanthis-Nikolaos Vreeken, Jilles De Bie, Tijl Intelligent Systems Laboratory University of Bristol Bristol United Kingdom Advanced Database Research and Modelling University of Antwerp Antwerp Belgium

ISBN: (纸本)9783642409905

In exploratory data mining it is important to assess the significance of results. Given that analysts have only limited time, it is important that we can measure this with regard to what we already know. That is, we want to be able to measure whether a result is interesting from a subjective point of view. With this as our goal, we formalise how to probabilistically model real-valued data by the Maximum Entropy principle, where we allow statistics on arbitrary sets of cells as background knowledge. As statistics, we consider means and variances, as well as histograms. The resulting models allow us to assess the likelihood of values, and can be used to verify the significance of (possibly overlapping) structures discovered in the data. As we can feed those structures back in, our model enables iterative identification of subjectively interesting structures. To show the flexibility of our model, we propose a subjective informativeness measure for tiles, i.e. rectangular sub-matrices, in real-valued data. The Information Ratio quantifies how strongly the knowledge of a structure reduces our uncertainty about the data with the amount of effort it would cost to consider it. Empirical evaluation shows that iterative scoring effectively reduces redundancy in ranking candidate tiles-showing the applicability of our model for a range of data mining fields aimed at discovering structure in real-valued data. © 2013 Springer-Verlag.

关键词： Data mining

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Slim: Directly mining descriptive patterns

Slim: Directly mining descriptive patterns

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12th SIAM International Conference on Data Mining, SDM 2012

作者： Smets, Koen Vreeken, Jilles Advanced Database Research and Modelling Universiteit Antwerpen Belgium

ISBN: (纸本)9781611972320

Mining small, useful, and high-quality sets of patterns has recently become an important topic in data mining. The standard approach is to first mine many candidates, and then to select a good subset. However, the pattern explosion generates such enormous amounts of candi- dates that by post-processing it is virtually impossible to analyse dense or large databases in any detail. We introduce Slim, an any-time algorithm for mining high-quality sets of itemsets directly from data. We use MDL to identify the best set of itemsets as that set that describes the data best. To approximate this optimum, we iteratively use the current solution to determine what itemset would provide most gain| estimating quality using an accurate heuristic. Without requiring a pre-mined candidate collection, Slim is parameter-free in both theory and practice. Experiments show we mine high-quality pattern sets;while evaluating orders-of-magnitude fewer can- didates than our closest competitor, Krimp, we obtain much better compression ratios|closely approximating the locally-optimal strategy. Classification experiments independently verify we characterise data very well. Copyright © 2012 by the Society for Industrial and Applied Mathematics.

关键词： Data mining

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Comparing apples and oranges: Measuring differences between exploratory data mining results

Comparing apples and oranges: Measuring differences between ...

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作者： Tatti, Nikolaj Vreeken, Jilles Advanced Database Research and Modelling Department of Mathematics and Computer Science University of Antwerp Antwerp Belgium

Deciding whether the results of two different mining algorithms provide significantly different information is an important, yet understudied, open problem in exploratory data mining. Whether the goal is to select the most informative result for analysis, or to decide which mining approach will most likely provide the most novel insight, it is essential that we can tell how different the information is that different results by possibly different methods provide. In this paper we take a first step towards comparing exploratory data mining results on binary data. We propose to meaningfully convert results into sets of noisy tiles, and compare between these sets by maximum entropy modelling and Kullback-Leibler divergence, well-founded notions from Information Theory. We so construct a measure that is highly flexible, and allows us to naturally include background knowledge, such that differences in results can be measured from the perspective of what a user already knows. Furthermore, adding to its interpretability, it coincides with Jaccard dissimilarity when we only consider exact tiles. Our approach provides a means to study and tell differences between results of different exploratory data mining methods. As an application, we show that our measure can also be used to identify which parts of results best redescribe other results. Furthermore, we study its use for iterative data mining, where one iteratively wants to find that result that will provide maximal novel information. Experimental evaluation shows our measure gives meaningful results, correctly identifies methods that are similar in nature, automatically provides sound redescriptions of results, and is highly applicable for iterative data mining. © The Author(s) 2012.

关键词： Data mining

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Maximum Entropy modelling for assessing results on real-valued data

Maximum Entropy modelling for assessing results on real-valu...

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11th IEEE International Conference on Data Mining, ICDM 2011

作者： Kontonasios, Kleanthis-Nikolaos Vreeken, Jilles De Bie, Tijl University of Bristol Intelligent Systems Laboratory United Kingdom University of Antwerp Advanced Database Research and Modelling Belgium

ISBN: (纸本)9780769544083

Statistical assessment of the results of data mining is increasingly recognised as a core task in the knowledge discovery process. It is of key importance in practice, as results that might seem interesting at first glance can often be explained by well-known basic properties of the data. In pattern mining, for instance, such trivial results can be so overwhelming in number that filtering them out is a necessity in order to identify the truly interesting patterns. In this paper, we propose an approach for assessing results on real-valued rectangular databases. More specifically, using our analytical model we are able to statistically assess whether or not a discovered structure may be the trivial result of the row and column marginal distributions in the database. Our main approach is to use the Maximum Entropy principle to fit a background model to the data while respecting its marginal distributions. To find these distributions, we employ an MDL based histogram estimator, and we fit these in our model using efficient convex optimization techniques. Subsequently, our model can be used to calculate probabilities directly, as well as to efficiently sample data with the purpose of assessing results by means of empirical hypothesis testing. Notably, our approach is efficient, parameter-free, and naturally deals with missing values. As such, it represents a well-founded alternative to swap randomisation. © 2011 IEEE.

关键词： Data mining

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Maximum Entropy modelling for Assessing Results on Real-Valued Data

Maximum Entropy Modelling for Assessing Results on Real-Valu...

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IEEE International Conference on Data Mining (ICDM)

作者： Kleanthis-Nikolaos Kontonasios Jilles Vreeken Tijl De Bie Intelligent Systems Laboratory University of Bristol UK Advanced Database Research and Modelling University of Antwerp Belgium

关键词： databases Histograms Entropy Data models Data mining Probabilistic logic Testing

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Predicting the severity of a reported bug

Predicting the severity of a reported bug

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7th IEEE Working Conference on Mining Software Repositories, MSR 2010, Co-located with the 2010 ACM/IEEE International Conference on Software Engineering, ICSE 2010

作者： Lamkanfi, Ahmed Demeyer, Serge Giger, Emanuel Goethals, Bart LORE - Lab on Reengineering University of Antwerp Belgium SEAL - Software Evolution and Architecture Lab University of Zürich Switzerland ADReM - Advanced Database Research and Modelling University of Antwerp Belgium

ISBN: (纸本)9781424468034

The severity of a reported bug is a critical factor in deciding how soon it needs to be fixed. Unfortunately, while clear guidelines exist on how to assign the severity of a bug, it remains an inherent manual process left to the person reporting the bug. In this paper we investigate whether we can accurately predict the severity of a reported bug by analyzing its textual description using text mining algorithms. Based on three cases drawn from the open-source community (Mozilla, Eclipse and GNOME), we conclude that given a training set of sufficient size (approximately 500 reports per severity), it is possible to predict the severity with a reasonable accuracy (both precision and recall vary between 0.65-0.75 with Mozilla and Eclipse;0.70-0.85 in the case of GNOME). © 2010 IEEE.

关键词： Forecasting

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Predicting the severity of a reported bug

Predicting the severity of a reported bug

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International Mining Software Repositories, MSR

作者： Ahmed Lamkanfi Serge Demeyer Emanuel Giger Bart Goethals LORE - Lab On Reengineering University of Antwerp Belgium SEAL - Software Evolution and Architecture Lab University of Zürich Switzerland ADReM - Advanced Database Research and Modelling University of Antwerp Belgium

关键词： Text mining Computer bugs Computer crashes Guidelines Algorithm design and analysis Programming Software systems Seals Software debugging Computer architecture

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Significance of Episodes Based on Minimal Windows

Significance of Episodes Based on Minimal Windows

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IEEE International Conference on Data Mining (ICDM)

作者： Nikolaj Tatti Advanced Database Research and Modelling ADReM University of Antwerp Antwerp Belgium

Discovering episodes, frequent sets of events from a sequence has been an active field in pattern mining. Traditionally, a level-wise approach is used to discover all frequent episodes. While this technique is computationally feasible it may result in a vast number of patterns, especially when low thresholds are used. In this paper we propose a new quality measure for episodes. We say that an episode is significant if the average length of its minimal windows deviates greatly when compared to the expected length according to the independence model. We can apply this measure as a post-pruning step to test whether the discovered frequent episodes are truly interesting and consequently to reduce the number of output. As a main contribution we introduce a technique that allows us to compute the distribution of lengths of minimal windows using the independence model. Such a computation task is surpisingly complex and in order to solve it we compute the distribution iteratively starting from simple episodes and progressively moving towards the more complex ones. In our experiments we discover candidate episodes that have a sufficient amount of minimal windows and test each candidate for significance. The experimental results demonstrate that our approach finds significant episodes while ignoring uninteresting ones.

关键词： Testing Distributed computing Data mining databases Random sequences Length measurement

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