  This paper presents a batch-wise density-based clustering approach for local
outlier detection in massive-scale datasets. Unlike the well-known traditional
algorithms, which assume that all the data is memory-resident, our proposed
method is scalable and processes the input data chunk-by-chunk within the
confines of a limited memory buffer. A temporary clustering model is built at
the first phase; then, it is gradually updated by analyzing consecutive memory
loads of points. Subsequently, at the end of scalable clustering, the
approximate structure of the original clusters is obtained. Finally, by another
scan of the entire dataset and using a suitable criterion, an outlying score is
assigned to each object called SDCOR (Scalable Density-based Clustering
Outlierness Ratio). Evaluations on real-life and synthetic datasets demonstrate
that the proposed method has a low linear time complexity and is more effective
and efficient compared to best-known conventional density-based methods, which
need to load all data into the memory; and also, to some fast distance-based
methods, which can perform on data resident in the disk.
