SPOT: A System for Detecting Projected Outliers From High-dimensional Data Streams

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Provided by: Dalhousie University
Topic: Big Data
Format: PDF
In this paper, the authors present a new technique, called Stream Projected Ouliter deTector (SPOT), to deal with outlier detection problem in high-dimensional data streams. SPOT is unique in a number of aspects. SPOT employs a novel window-based time model and decaying cell summaries to capture statistics from the data stream. Sparse Subspace Template (SST), a set of top sparse subspaces obtained by unsupervised and/or supervised learning processes, is constructed in SPOT to detect projected outliers effectively.
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