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Cited by 365

The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.

Outlier mining in large high-dimensional data sets

Angiulli, F., Pizzuti, C.·2005·IEEE Transactions on Knowledge and Data Engineering
DOI 10.1109/tkde.2005.31
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Abstract

A new definition of distance-based outlier and an algorithm, called HilOut, designed to efficiently detect the top n outliers of a large and high-dimensional data set are proposed. Given an integer k, the weight of a point is defined as the sum of the distances separating it from its k nearest-neighbors. Outlier are those points scoring the largest values of weight. The algorithm HilOut makes use of the notion of space-filling curve to linearize the data set, and it consists of two phases. The first phase provides an approximate solution, within a rough factor, after the execution of at most d + 1 sorts and scans of the data set, with temporal cost quadratic in d and linear in N and in k, where d is the number of dimensions of the data set and N is the number of points in the data set. During this phase, the algorithm isolates points candidate to be outliers and reduces this set at each iteration. If the size of this set becomes n, then the algorithm stops reporting the exact solution. The second phase calculates the exact solution with a final scan examining further the candidate outliers that remained after the first phase. Experimental results show that the algorithm always stops, reporting the exact solution, during the first phase after much less than d + 1 steps. We present both an in-memory and disk-based implementation of the HilOut algorithm and a thorough scaling analysis for real and synthetic data sets showing that the algorithm scales well in both cases.

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Metadata

Authors
Angiulli, F., & Pizzuti, C.
Venue
IEEE Transactions on Knowledge and Data Engineering
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2005-02
Volume
17
Issue
2
Pages
203-215
ISSN
1041-4347 (ISSN-L)
DOI
10.1109/tkde.2005.31
OpenAlex
W2170314592
Cited by
365
References
36
FWCI
13.40
Type
journal article
Language
en
Open access
closed

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