TY - GEN A1 - Schäfer, Patrick T1 - Bag-Of-SFA-Symbols in Vector Space (BOSS VS) N2 - Time series classification mimics the human understanding of similarity. When it comes to larger datasets, state of the art classifiers reach their limits in terms of unreasonable training or testing times. One representative example is the 1-nearest-neighbor DTW classifier (1-NN DTW) that is commonly used as the benchmark to compare to and has several shortcomings: it has a quadratic time and it degenerates in the presence of noise. To reduce the computational complexity lower bounding techniques or recently a nearest centroid classifier have been introduced. Still, execution times to classify moderately sized datasets on a single core are in the order of hours. We present our Bag-Of-SFA-Symbols in Vector Space (BOSS VS) classifier that is robust and accurate due to invariance to noise, phase shifts, offsets, amplitudes and occlusions. We show that it is as accurate while being multiple orders of magnitude faster than state of the art classifiers. Using the BOSS VS allows for mining massive time series datasets and real-time analytics. T3 - ZIB-Report - 15-30 KW - Time Series KW - Classification KW - Data Mining Y1 - 2015 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-54984 SN - 1438-0064 ER - TY - GEN A1 - Galliat, Tobias T1 - Clustering Data of Different Information Levels N2 - For using Data Mining, especially cluster analysis, one needs measures to determine the similarity or distance between data objects. In many application fields the data objects can have different information levels. In this case the widely used euclidean distance is an inappropriate measure. The present paper describes a concept how to use data of different information levels in cluster analysis and suggests an appropriate similarity measure. An example from practice is included, that shows the usefulness of the concept and the measure in combination with {\sc Kohonens} Self-Organizing Map algorithm, a well-known and powerful tool for cluster analysis. T3 - ZIB-Report - SC-99-42 KW - cluster analysis KW - Data Mining KW - data preprocessing KW - information theory KW - missing values KW - Self-Organizing Maps KW - similarity measures Y1 - 1999 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-4319 ER -