Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
Finding surprising patterns in a time series database in linear time and space
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Clustering Data Streams: Theory and Practice
IEEE Transactions on Knowledge and Data Engineering
Automatic Feature Extraction for Classifying Audio Data
Machine Learning
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Knowledge Discovery from Databases (KDD) - also named Data Mining - is a growing field since 10 years which combines techniques from databases, statistics, and machine learning. Applications of KDD most often have one of the following goals: - Customer relationship management: who are the best customers, which products are to be offered to which customers (direct marketing or customer acquisition), which customers are likely to end the relationship (customer churn), which customers are likely to not pay (also coined as fraud detection)? - Decision support applies to almost all areas, ranging from medicine over marketing to logistics. KDD applications aim at a data-driven justification of decisions by relating actions and outcomes. - Recommender systems rank objects according to user profiles. The objects can be, for instance, products as in the amazon internet shop, or documents as in learning search engines. KDD applications do not assume user profiles to be given but learns tehm from observations of user behavior. - Plant asset management moves beyond job scheduling and quality control. The goal is to optimize the overall benefits of production.