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14 results on '"Goethals, Bart"'

1. SubSect—An Interactive Itemset Visualization

Author

De Pauw, Joey, Moens, Sandy, Goethals, Bart, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Bogaerts, Bart, editor, Bontempi, Gianluca, editor, Geurts, Pierre, editor, Harley, Nick, editor, Lebichot, Bertrand, editor, Lenaerts, Tom, editor, and Louppe, Gilles, editor

Published
2020
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2. Efficient Discovery of Sets of Co-occurring Items in Event Sequences

Author

Cule, Boris, Feremans, Len, Goethals, Bart, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Frasconi, Paolo, editor, Landwehr, Niels, editor, Manco, Giuseppe, editor, and Vreeken, Jilles, editor

Published
2016
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3. k-Morik: Mining Patterns to Classify Cartified Images of Katharina

Author

Fromont, Elisa, Goethals, Bart, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Michaelis, Stefan, editor, Piatkowski, Nico, editor, and Stolpe, Marco, editor

Published
2016
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4. Mining Cohesive Itemsets in Graphs

Author

Hendrickx, Tayena, Cule, Boris, Goethals, Bart, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Goebel, Randy, Series editor, Tanaka, Yuzuru, Series editor, Wahlster, Wolfgang, Series editor, Siekmann, Jörg, Series editor, Džeroski, Sašo, editor, Panov, Panče, editor, Kocev, Dragi, editor, and Todorovski, Ljupčo, editor

Published
2014
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5. Itemset Based Sequence Classification

Author

Zhou, Cheng, Cule, Boris, Goethals, Bart, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Blockeel, Hendrik, editor, Kersting, Kristian, editor, Nijssen, Siegfried, editor, and Železný, Filip, editor

Published
2013
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6. Mining Interesting Itemsets in Graph Datasets

Author

Cule, Boris, Goethals, Bart, Hendrickx, Tayena, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Pei, Jian, editor, Tseng, Vincent S., editor, Cao, Longbing, editor, Motoda, Hiroshi, editor, and Xu, Guandong, editor

Published
2013
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8. A primer to frequent itemset mining for bioinformatics.

Author

Naulaerts, Stefan, Meysman, Pieter, Bittremieux, Wout, Vu, Trung Nghia, Berghe, Wim Vanden, Goethals, Bart, and Laukens, Kris

Subjects
DATA mining, BIOINFORMATICS, SUPERMARKETS, ALGORITHMS, LIFE sciences
Abstract

Over the past two decades, pattern mining techniques have become an integral part of many bioinformatics solutions. Frequent itemset mining is a popular group of pattern mining techniques designed to identify elements that frequently co-occur . An archetypical example is the identification of products that often end up together in the same shopping basket in supermarket transactions. A number of algorithms have been developed to address variations of this computationally non-trivial problem. Frequent itemsetmining techniques are able to efficiently capture the characteristics of (complex) data and succinctly summarize it. Owing to these and other interesting properties, these techniques have proven their value in biological data analysis. Nevertheless, information about the bioinformatics applications of these techniques remains scattered. In this primer, we introduce frequent itemset mining and their derived association rules for life scientists. We give an overview of various algorithms, and illustrate how they can be used in several real-life bioinformatics application domains. We end with a discussion of the future potential and open challenges for frequent itemset mining in the life sciences. [ABSTRACT FROM AUTHOR]

Published
2015
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9. A pattern based predictor for event streams.

Author

Zhou, Cheng, Cule, Boris, and Goethals, Bart

Subjects
*DATA mining, *DATA modeling, *PATTERN recognition systems, *PREDICTION models, *LOSSY data compression
Abstract

Recently, new emerging applications, such as web click-stream mining, failure forecast and traffic analysis, introduced a new challenging data model referred to as data streams. Mining such data can reveal up-to-date patterns, which are useful for predicting future events. Consequently, pattern mining in data streams is a popular field in data mining that presents unique challenges. The data is large and endlessly keeps on coming, making it impossible to store it, or to re-analyse historical data once it has been discarded. To solve this, we first present a novel method for mining sequential patterns from a data stream, in which we maximise memory usage in order to achieve higher accuracy in terms of results. In a second step, we use the discovered patterns in order to try to predict future events. We propose a number of ways to assign a score to each pattern in order to generate predictions. The prediction performance of these scoring strategies is then extensively experimentally evaluated. The predictor offers an opportunity for a faster detection and response to an important, though perhaps unexpected, event, which will occur in the future. [ABSTRACT FROM AUTHOR]

Published
2015
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14. Constraint Relaxations for Discovering Unknown Sequential Patterns

Author

Antunes, Cláudia, Oliveira, Arlindo L., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goethals, Bart, editor, and Siebes, Arno, editor

Published
2005
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