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Finding Intensional Knowledge of Distance-Based Outliers

Edwin M. Knorr and Raymond T. Ng

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Abstract
Existing studies on outliers focus only on the identification aspect; none provides any intensional knowledge of the outliers - by which we mean a description or an explanation of why an identified outlier is exceptional. For many applications, a description or explanation is at least as vital to the user as the identification aspect. Specifically, intensional knowledge helps the user to: (i) evaluate the validity of the identified outliers, and (ii) improve one's understanding of the data.

The two main issues addresses in this paper are: what kinds of intensional knowledge to provide, and how to optimize the computation of such knowledge. With respect to the first issue, we propose finding strongest and weak outliers and their corresponding structural intensional knowledge. With respect to the second issue, we first present a naive and a semi-naive algorithm. Then, by means of what we call path and semi-lattice sharing of I/O processing, we develop two optimized approaches. We provide analytic results on their I/O performance, and present experimental results showing significant reductions in I/O and significant speedups in overall runtime.


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BIBTEX

@inproceedings{DBLP:conf/vldb/KnorrN99,
  author    = {Edwin M. Knorr and
                Raymond T. Ng},
   editor    = {Malcolm P. Atkinson and
                Maria E. Orlowska and
                Patrick Valduriez and
                Stanley B. Zdonik and
                Michael L. Brodie},
   title     = {Finding Intensional Knowledge of Distance-Based Outliers},
   booktitle = {VLDB'99, Proceedings of 25th International Conference on Very
                Large Data Bases, September 7-10, 1999, Edinburgh, Scotland,
                UK},
   publisher = {Morgan Kaufmann},
   year      = {1999},
   isbn      = {1-55860-615-5},
   pages     = {211-222},
   crossref  = {DBLP:conf/vldb/99},
   bibsource = {DBLP, http://dblp.uni-trier.de} } },


























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