• Publications
  • Influence
Krimp: mining itemsets that compress
One of the major problems in pattern mining is the explosion of the number of results. Tight constraints reveal only common knowledge, while loose constraints lead to an explosion in the number ofExpand
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Fast and reliable anomaly detection in categorical data
Spotting anomalies in large multi-dimensional databases is a crucial task with many applications in finance, health care, security, etc. We introduce COMPREX, a new approach for identifying anomaliesExpand
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The long and the short of it: summarising event sequences with serial episodes
An ideal outcome of pattern mining is a small set of informative patterns, containing no redundancy or noise, that identifies the key structure of the data at hand. Standard frequent pattern minersExpand
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The Odd One Out: Identifying and Characterising Anomalies
In many situations there exists an abundance of positive examples, but only a handful of negatives. In this paper we show how in binary or transaction data such rare cases can be identified andExpand
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Spotting Culprits in Epidemics: How Many and Which Ones?
Given a snapshot of a large graph, in which an infection has been spreading for some time, can we identify those nodes from which the infection started to spread? In other words, can we reliably tellExpand
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Item Sets that Compress
One of the major problems in frequent item set mining is the explosion of the number of results: it is difficult to find the most interesting frequent item sets. The cause of this explosion is thatExpand
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Spiking neural networks, an introduction
Biological neurons use short and sudden increases in voltage to send information. These signals are more commonly known as action potentials, spikes or pulses. Recent neurological research has shownExpand
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Model order selection for boolean matrix factorization
Matrix factorizations---where a given data matrix is approximated by a product of two or more factor matrices---are powerful data mining tools. Among other tasks, matrix factorizations are often usedExpand
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VOG: Summarizing and Understanding Large Graphs
How can we succinctly describe a million-node graph with a few simple sentences? How can we measure the "importance" of a set of discovered subgraphs in a large graph? These are exactly the problemsExpand
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Tell me what i need to know: succinctly summarizing data with itemsets
Data analysis is an inherently iterative process. That is, what we know about the data greatly determines our expectations, and hence, what result we would find the most interesting. With this inExpand
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