Kriegel, Hans-Peter

LOF: identifying density-based local outliers

Breunig, M. M., Kriegel, H.-P., Ng, R. T., & Sander, J. (2000) · ACM SIGMOD Record

Anomaly Detection Techniques and ApplicationsData Management and Algorithms

Cited by 5,207

LOF: identifying density-based local outliers

Breunig, M. M., Kriegel, H.-P., Ng, R. T., & Sander, J. (2000) · Proceedings of the 2000 ACM SIGMOD international conference on Management of data

Anomaly Detection Techniques and ApplicationsAdvanced Statistical Methods and Models

Cited by 4,149

Integrating structured biological data by Kernel Maximum Mean Discrepancy

Borgwardt, K. M., Gretton, A., Rasch, M. J., Kriegel, H.-P., Schölkopf, B., & Smola, A. J. (2006) · Bioinformatics

Gene expression and cancer classificationBioinformatics and Genomic Networks

Cited by 1,674

Density‐based clustering

Kriegel, H., Kröger, P., Sander, J., & Zimek, A. (2011) · WIREs Data Mining and Knowledge Discovery

Advanced Clustering Algorithms ResearchData Management and Algorithms

Cited by 900

A survey on unsupervised outlier detection in high‐dimensional numerical data

Zimek, A., Schubert, E., & Kriegel, H. (2012) · Statistical Analysis and Data Mining The ASA Data Science Journal

Anomaly Detection Techniques and ApplicationsAdvanced Statistical Methods and Models

Cited by 864

Factorizing YAGO: scalable machine learning for linked data

Nickel, M., Tresp, V., & Kriegel, H.-P. (2012) · Proceedings of the 21st international conference on World Wide Web

Semantic Web and OntologiesAdvanced Graph Neural Networks

Cited by 370