His most important contributions are the database index structures R*-tree, X-tree and IQ-Tree, the cluster analysis algorithms DBSCAN, OPTICS and SUBCLU and the anomaly detection method Local Outlier Factor (LOF).
His research group developed a software framework titled ELKI that is designed for the parallel research of index structures, data mining algorithms and their interaction, such as optimized data mining algorithms based on database indexes.
In 2009 the Association for Computing Machinery appointed Hans-Peter Kriegel a "fellow",[2] one of its highest honors.
He received the 2013 IEEE ICDM Research Contributions Award for his research on data mining algorithms such as DBSCAN, OPTICS, Local Outlier Factor and his work on mining high-dimensional data.
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