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Recommender for Dating portals

Online dating has mostly has been about just wanting to find "the one". For many users, the idea of sorting through hundreds of thousands of online dating profiles to find potential matches seems daunting. Instead, it would be great to have an automated system that recommends profiles of other users that a user will like. One way to accomplish this is to build a recommendation system. For example one that predicts the profiles a user is likely to enjoy based upon the user's past ratings of other profiles. The success of recommendation systems ,which are just as applicable to products as people, says much about the ability of computers to predict the more fundamental attractions.

Kontakt: Lydia Voß
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Finding Anomalities in Time Series

Time series refer to streams of data ordered in a time based sequence. Such data is observed in plenty of real world domains as statistics, signal, processing, medical measurements (ECG,EEG), etc ... Analysis of time series has attracted considerable interest, still various aspects remain under research focus. One of the most important challenges is to identify anomalies in the series, which denote surprising or interesting patterns. An example of anomaly detection could be detecting anomaly subsequences in the heart signal of an ECG time series plot.

Your task is to implement the referenced research paper which presents a technique on detecting surprising patterns. In the end a software which detects and displays anomalies is expected.

Reference: Eamonn J. Keogh, Stefano Lonardi, Bill Yuan-chi Chiu: Finding surprising patterns in a time series database in linear time and space. KDD 2002: 550-556

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Abgeschlossene Bachelorarbeiten an der Universität Hildesheim
Abgeschlossene Bachelor- und Studienarbeiten an der Universität Freiburg