Výsledky vyhledávání

RASCL: a randomised approach to subspace clusters

Autor: Sandy Moens, Boris Cule, Bart Goethals

Publikováno v: International Journal of Data Science and Analytics, 14(3), 243-259. Springer International Publishing
International journal of data science and analytics

Subspace clustering aims to discover clusters in projections of highly dimensional numerical data. In this paper, we focus on discovering small collections of highly interesting subspace clusters that do not try to cluster all data points, leaving no

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::21bef22d15f004c3aa420592eff3a6f8
https://research.tilburguniversity.edu/en/publications/044f3ffa-fd46-4be8-81ff-bbd61d9262cb

Zobrazit plný text záznamu

Interactive evaluation of recommender systems with SNIPER

Autor: Sandy Moens, Olivier Jeunen, Bart Goethals

Publikováno v: RecSys
Proceedings of the 13th ACM Conference on Recommender Systems (RecSys '19), September 16-20, 2019, Copenhagen, Denmark

Recommender systems are typically evaluated using either offline methods, online methods, or through user studies. In this paper we take an episode mining approach to analysing recommender system data and we demonstrate how we can use SNIPER, a tool

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ca3b49afc5b9dd9b43fe78cdcfccd839
https://doi.org/10.1145/3298689.3346965

Zobrazit plný text záznamu

A Sampling-Based Approach for Discovering Subspace Clusters

Autor: Boris Cule, Bart Goethals, Sandy Moens

Publikováno v: Discovery Science ISBN: 9783030337773
DS
Discovery science : 22nd International Conference, DS 2019, Split, Croatia, October 28–30, 2019: proceedings
Lecture notes in computer science ; 11828

Subspace clustering aims to discover clusters in projections of highly dimensional numerical data. In this paper, we focus on discovering small collections of interesting subspace clusters that do not try to cluster all data points, leaving noisy dat

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cfa763ae48e329da82963d9dd17af1ea
https://doi.org/10.1007/978-3-030-33778-0_6

Zobrazit plný text záznamu

Providing Concise Database Covers Instantly by Recursive Tile Sampling

Autor: Mario Boley, Bart Goethals, Sandy Moens

Publikováno v: Discovery Science ISBN: 9783319118116
Discovery Science

Known pattern discovery algorithms for finding tilings (covers of 0/1-databases consisting of 1-rectangles) cannot be integrated in instant and interactive KD tools, because they do not satisfy at least one of two key requirements: a) to provide resu

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::66b7a148e8d224931d7fcbe73b1691d0
https://doi.org/10.1007/978-3-319-11812-3_19

Zobrazit plný text záznamu

Interactive and manual construction of classification trees

Autor: Stephen Pauwel, Sandy Moens, Bart Goethals

Publikováno v: Proceedings of the 23rd Belgian-Dutch Conference on Machine Learning (BENELEARN 2014), 2014
University of Antwerp

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::da54a46bcbe685e76677c6e825305eaa
https://hdl.handle.net/10067/1231960151162165141

Zobrazit plný text záznamu

Instant Exceptional Model Mining Using Weighted Controlled Pattern Sampling

Autor: Mario Boley, Sandy Moens

Publikováno v: Advances in Intelligent Data Analysis XIII ISBN: 9783319125701
IDA

When plugged into instant interactive data analytics processes, pattern mining algorithms are required to produce small collections of high quality patterns in short amounts of time. In the case of Exceptional Model Mining (EMM), even heuristic appro

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::eb40b40fca21562a040f97d21f596944
https://doi.org/10.1007/978-3-319-12571-8_18

Zobrazit plný text záznamu

Frequent Itemset Mining for Big Data

Autor: Bart Goethals, Sandy Moens, Emin Aksehirli

Publikováno v: BigData Conference
IEEE Big Data 2013 : International Conference on Big Data, October 6-9, 2013, Santa Clara, Calif., USA

Frequent Itemset Mining (FIM) is one of the most well known techniques to extract knowledge from data. The combinatorial explosion of FIM methods become even more problematic when they are applied to Big Data. Fortunately, recent improvements in the

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a4da20005cb8aa8ce30c7d1bf301d5f4
https://doi.org/10.1109/bigdata.2013.6691742

Zobrazit plný text záznamu

Randomly sampling maximal itemsets

Autor: Bart Goethals, Sandy Moens

Publikováno v: KDD 2013 Workshop on Interactive Data Exploration and Analytics (IDEA), August 11, Chicago, Ill., USA
IDEA@KDD

Pattern mining techniques generally enumerate lots of uninteresting and redundant patterns. To obtain less redundant collections, techniques exist that give condensed representations of these collections. However, the proposed techniques often rely o

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9f1fe78f4b3c4b36f4ff3d3cbf18d174
https://hdl.handle.net/10067/1153340151162165141

Zobrazit plný text záznamu

Linear space direct pattern sampling using coupling from the past

Autor: Thomas Gärtner, Sandy Moens, Mario Boley

Publikováno v: KDD
KDD '12 Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

This paper shows how coupling from the past (CFTP) can be used to avoid time and memory bottlenecks in direct local pattern sampling procedures. Such procedures draw controlled amounts of suitably biased samples directly from the pattern space of a g

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1026d4f95390326a561753aa5a0c2c64
https://doi.org/10.1145/2339530.2339545

Zobrazit plný text záznamu

Vyhledávací nástroje:

Upřesnit hledání