41 Result(s)
within
Matthijs van Leeuwen
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Article
Open AccessExplainable contextual anomaly detection using quantile regression forests
Traditional anomaly detection methods aim to identify objects that deviate from most other objects by treating all features equally. In contrast, contextual anomaly detection methods aim to detect objects that...
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Article
Open AccessUnsupervised discretization by two-dimensional MDL-based histogram
Unsupervised discretization is a crucial step in many knowledge discovery tasks. The state-of-the-art method for one-dimensional data infers locally adaptive histograms using the minimum description length (MD...
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Chapter and Conference Paper
Discovering Diverse Top-K Characteristic Lists
In this work, we define the new problem of finding diverse top-k characteristic lists to provide different statistically robust explanations of the same dataset. This type of problem is often encountered in co...
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Chapter and Conference Paper
Novel Approach for Phenoty** Based on Diverse Top-K Subgroup Lists
The discovery of phenotypes is useful to describe a population. Providing a set of diverse patient phenotypes with the same medical condition may help clinicians to understand it. In this paper, we approach th...
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Chapter and Conference Paper
Discovering Rule Lists with Preferred Variables
Interpretable machine learning focuses on learning models that are inherently understandable by humans. Even such interpretable models, however, must be trustworthy for domain experts to adopt them. This requi...
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Chapter and Conference Paper
Truly Unordered Probabilistic Rule Sets for Multi-class Classification
Rule set learning has long been studied and has recently been frequently revisited due to the need for interpretable models. Still, existing methods have several shortcomings: 1) most recent methods require a ...
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Article
Open AccessRobust subgroup discovery
We introduce the problem of robust subgroup discovery, i.e., finding a set of interpretable descriptions of subsets that 1) stand out with respect to one or more target attributes, 2) are statistically robust, an...
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Chapter and Conference Paper
Discovering Outstanding Subgroup Lists for Numeric Targets Using MDL
The task of subgroup discovery (SD) is to find interpretable descriptions of subsets of a dataset that stand out with respect to a target attribute. To address the problem of mining large numbers of redundant ...
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Article
Open AccessOnline summarization of dynamic graphs using subjective interestingness for sequential data
Many real-world phenomena can be represented as dynamic graphs, i.e., networks that change over time. The problem of dynamic graph summarization, i.e., to succinctly describe the evolution of a dynamic graph, ...
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Article
Discovering subjectively interesting multigraph patterns
Over the past decade, network analysis has attracted substantial interest because of its potential to solve many real-world problems. This paper lays the conceptual foundation for an application in aviation, t...
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Chapter and Conference Paper
Challenges and Limitations in Clustering Blood Donor Hemoglobin Trajectories
In order to prevent iron deficiency, Sanquin—the national blood bank in the Netherlands—measures a blood donor’s hemoglobin (Hb) level before each donation and only allows a donor to donate blood if their Hb i...
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Chapter and Conference Paper
Widening for MDL-Based Retail Signature Discovery
Signature patterns have been introduced to model repetitive behavior, e.g., of customers repeatedly buying the same set of products in consecutive time periods. A disadvantage of existing approaches to signature ...
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Chapter and Conference Paper
Vouw: Geometric Pattern Mining Using the MDL Principle
We introduce geometric pattern mining, the problem of finding recurring local structure in discrete, geometric matrices. It differs from existing pattern mining problems by identifying complex spatial relation...
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Article
Relational data factorization
Motivated by an analogy with matrix factorization, we introduce the problem of factorizing relational data. In matrix factorization, one is given a matrix and has to factorize it as a product of other matrices...
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Article
Flexible constrained sampling with guarantees for pattern mining
Pattern sampling has been proposed as a potential solution to the infamous pattern explosion. Instead of enumerating all patterns that satisfy the constraints, individual patterns are sampled proportional to a...
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Chapter and Conference Paper
Explaining Deviating Subsets Through Explanation Networks
We propose a novel approach to finding explanations of deviating subsets, often called subgroups. Existing approaches for subgroup discovery rely on various quality measures that nonetheless often fail to find su...
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Chapter and Conference Paper
Learning What Matters – Sampling Interesting Patterns
In the field of exploratory data mining, local structure in data can be described by patterns and discovered by mining algorithms. Although many solutions have been proposed to address the redundancy problems in ...
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Article
Open AccessSubjective interestingness of subgraph patterns
The utility of a dense subgraph in gaining a better understanding of a graph has been formalised in numerous ways, each striking a different balance between approximating actual interestingness and computation...
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Chapter and Conference Paper
An Exercise in Declarative Modeling for Relational Query Mining
Motivated by the declarative modeling paradigm for data mining, we report on our experience in modeling and solving relational query and graph mining problems with the IDP system, a variation on the answer set...
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Chapter and Conference Paper
Expect the Unexpected – On the Significance of Subgroups
Within the field of exploratory data mining, subgroup discovery is concerned with finding regions in the data that stand out with respect to a particular target. An important question is how to validate the pa...
