Stephan Spiegel

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Nowadays we are faced with fast growing and permanently evolving data, including social networks and sensor data recorded from smart phones or vehicles. Temporally evolving data brings a lot of new challenges to the data mining and machine learning community. This paper is concerned with the recognition of recurring patterns within multivariate time series,(More)
This paper discusses the combination of <i>collaborative</i> and <i>content-based</i> filtering in the context of web-based recommender systems. In particular, we link the well-known <i>MovieLens</i> rating data with supplementary <i>IMDB</i> content information. The resulting network of user-item relations and associated content features is converted into(More)
Within the last few years a lot of research has been done on large social and information networks. One of the principal challenges concerning complex networks is link prediction. Most link prediction algorithms are based on the underlying network structure in terms of traditional graph theory. In order to design efficient algorithms for large scale(More)
We propose an approach for violence analysis of movies in a multi-modal (visual and audio) manner with one-class and two-class support vector machine (SVM). We use the scale-invariant feature transform (SIFT) features with the Bag-of-Words (BoW) approach for visual content description of movies, where audio content description is performed with the(More)
Nowadays computer scientists are faced with fast growing and permanently evolving data, which are represented as observations made sequentially in time. A common problem in the data mining community is the recognition of recurring patterns within temporal databases or streaming data. This dissertation proposal aims at developing and investigating efficient(More)
Although there has been substantial progress in time series analysis in recent years, time series distance measures still remain a topic of interest with a lot of potential for improvements. In this paper we introduce a novel Order Invariant Distance measure which is able to determine the (dis)similarity of time series that exhibit similar sub-sequences at(More)
In time series mining, the Dynamic Time Warping (DTW) distance is a commonly and widely used similarity measure. Since the computational complexity of the DTW distance is quadratic, various kinds of warping constraints, lower bounds and abstractions have been developed to speed up time series mining under DTW distance. In this contribution, we propose a(More)