Auflistung nach Autor:in "Assent, Ira"
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- ZeitschriftenartikelAn Index-Inspired Algorithm for Anytime Classification on Evolving Data Streams(Datenbank-Spektrum: Vol. 12, No. 1, 2012) Kranen, Philipp; Assent, Ira; Seidl, ThomasDue 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 on streams of varying data rates, and are also shown to outperform traditional budget approaches on constant streams. In this article we present an index-inspired algorithm for Bayesian anytime classification on evolving data streams and show its performance on benchmark data sets.
- KonferenzbeitragEfficient adaptive retrieval and mining in large multimedia databases(Datenbanksysteme in Business, Technologie und Web (BTW) – 13. Fachtagung des GI-Fachbereichs "Datenbanken und Informationssysteme" (DBIS), 2009) Assent, IraMultimedia databases are increasingly common in science, business, entertainment and many other applications. Their size and high dimensionality of features are major challenges for efficient and effective retrieval and mining. Effective similarity models
- KonferenzbeitragA framework for evaluation and exploration of clustering algorithms in subspaces of high dimensional databases(Datenbanksysteme für Business, Technologie und Web (BTW), 2011) Müller, Emmanuel; Assent, Ira; Günnemann, Stephan; Gerwert, Patrick; Hannen, Matthias; Jansen, Timm; Seidl, ThomasIn high dimensional databases, traditional full space clustering methods are known to fail due to the curse of dimensionality. Thus, in recent years, subspace clustering and projected clustering approaches were proposed for clustering in high dimensional spaces. As the area is rather young, few comparative studies on the advantages and disadvantages of the different algorithms exist. Part of the underlying problem is the lack of available open source implementations that could be used by researchers to understand, compare, and extend subspace and projected clustering algorithms. In this work, we discuss the requirements for open source evaluation software and propose the OpenSubspace framework that meets these requirements. OpenSubspace integrates state-of-the-art performance measures and visualization techniques to foster clustering research in high dimensional databases.
- KonferenzbeitragHigh-dimensional indexing for multimedia features(Datenbanksysteme in Business, Technologie und Web (BTW) – 13. Fachtagung des GI-Fachbereichs "Datenbanken und Informationssysteme" (DBIS), 2009) Assent, Ira; Günnemann, Stephan; Kremer, Hardy; Seidl, ThomasEfficient content-based similarity search in large multimedia databases requires efficient query processing algorithms for many practical applications. Especially in high-dimensional spaces, the huge number of features is a challenge to existing indexing
- ZeitschriftenartikelInternationale Frauenuniversität – Projektbereich Information(Vol. 23, Frauen machen sich breit., 2001) Assent, Ira