An Index-Inspired Algorithm for Anytime Classification on Evolving Data Streams

Autor: Philipp Kranen, Thomas Seidl, Ira Assent

Publikováno v: Kranen, P, Assent, I & Seidl, T 2012, ' An Index-Inspired Algorithm for Anytime Classification on Evolving Data Streams ', Datenbank-Spektrum, vol. 12, no. 1, pp. 43-50 . https://doi.org/10.1007/s13222-012-0083-9

Due to the ever growing presence of data streams there has been a considerable amount of research on stream data mining over the past years. Anytime algorithms are particularly well suited for stream mining, since they flexibly use all available time

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8d6243cc920f4602747e7884daa4b45a
https://doi.org/10.1007/s13222-012-0083-9

Inverse predictions on continuous models in scientific databases

Autor: Anca M. Zimmer, Thomas Seidl, Philip Driessen, Philipp Kranen

Publikováno v: SSDBM

Using continuous models in scientific databases has received an increased attention in the last years. It allows for a more efficient and accurate querying, as well as predictions of the outputs even where no measurements were performed. The most com

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::0eb01c384431fc7a7818be850b6a1118
https://doi.org/10.1145/2618243.2618249

A tool for automated evaluation of algorithms

Autor: Philipp Kranen, Sebastian Raubach, Tim Rohlfs, Thomas Seidl, Stephan Wels

Publikováno v: CIKM

Testing algorithms and systems involves trying different sets of parameter values on different domains or data sets. Even for a moderate number of parameters and domains the number of possible experiments can get very large due to the combinatorial e

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::f2a785ad8f881748a403d0d6f870d82e
https://doi.org/10.1145/2396761.2398728

Stream Data Mining Using the MOA Framework

Autor: Philipp Kranen, Jesse Read, Timm Jansen, Geoff Holmes, Thomas Seidl, Albert Bifet, Bernhard Pfahringer, Hardy Kremer

Publikováno v: Database Systems for Advanced Applications ISBN: 9783642290343
DASFAA (2)

Massive Online Analysis (MOA) is a software framework that provides algorithms and evaluation methods for mining tasks on evolving data streams. In addition to supervised and unsupervised learning, MOA has recently been extended to support multi-labe

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::a7277609d0f14b08fddf09c13f920028
https://doi.org/10.1007/978-3-642-29035-0_27

An effective evaluation measure for clustering on evolving data streams

Autor: Thomas Seidl, Timm Jansen, Albert Bifet, Philipp Kranen, Hardy Kremer, Geoff Holmes, Bernhard Pfahringer

Publikováno v: KDD

Due to the ever growing presence of data streams, there has been a considerable amount of research on stream mining algorithms. While many algorithms have been introduced that tackle the problem of clustering on evolving data streams, hardly any atte

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::271603777fab64cefbdc66093556663f
https://doi.org/10.1145/2020408.2020555