Hephaistos: A fast and distributed outlier detection approach for big mixed attribute data

This paper tackles a new problem in outlier detection: how to promptly detect the local outlier of a large-scale mixed attribute data in the big data era. This poses significant challenges due to a lack of access to the entire mixed attribute dataset at any individual compute machine. Proposed approaches firstly form a mechanism that deletes the massive clear non-noise and extracts cluster-based pre-noise set. Furthermore, we analyze pre-noise set using multi-step distributed LOF computing method on the Spark platform. Finally, the ordered LOF list is the output result. Comprehensive experiments are implemented by large-scale Benchmark datasets and the Spark platform. Extensive results show that the performance of our approaches are superior to the previous ones (4X faster than baseline LOF/2X faster than DLOF) when compared to state-of-the-art techniques, and therefore is believed to be able to give better guidance to local outlier detection of mixed attribute data.