Finding Intensional Knowledge of Distance-Based Outliers.

@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      = {1-55860-615-7},
  pages     = {211-222},
  ee        = {db/conf/vldb/KnorrN99.html},
  crossref  = {DBLP:conf/vldb/99},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

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.

Copyright © 1999 by the VLDB Endowment. Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the VLDB copyright notice and the title of the publication and its date appear, and notice is given that copying is by the permission of the Very Large Data Base Endowment. To copy otherwise, or to republish, requires a fee and/or special permission from the Endowment.

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References

[AGGR98]

Rakesh Agrawal, Johannes Gehrke, Dimitrios Gunopulos, Prabhakar Raghavan: Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications. SIGMOD Conference 1998: 94-105 BibTeX

[AGI+92]

Rakesh Agrawal, Sakti P. Ghosh, Tomasz Imielinski, Balakrishna R. Iyer, Arun N. Swami: An Interval Classifier for Database Mining Applications. VLDB 1992: 560-573 BibTeX

[AIS93]

Rakesh Agrawal, Tomasz Imielinski, Arun N. Swami: Mining Association Rules between Sets of Items in Large Databases. SIGMOD Conference 1993: 207-216 BibTeX

[BL94]

...

[BFOS84]

...

[BMS97]

Sergey Brin, Rajeev Motwani, Craig Silverstein: Beyond Market Baskets: Generalizing Association Rules to Correlations. SIGMOD Conference 1997: 265-276 BibTeX

[HKPT98]

Ykä Huhtala, Juha Kärkkäinen, Pasi Porkka, Hannu Toivonen: Efficient Discovery of Functional and Approximate Dependencies Using Partitions. ICDE 1998: 392-401 BibTeX

[JKN98]

Theodore Johnson, Ivy Kwok, Raymond T. Ng: Fast Computation of 2-Dimensional Depth Contours. KDD 1998: 224-228 BibTeX

[KR90]

...

[KR98]

Edwin M. Knorr, Raymond T. Ng: Algorithms for Mining Distance-Based Outliers in Large Datasets. VLDB 1998: 392-403 BibTeX

[KN99]

...

[NLHP98]

Raymond T. Ng, Laks V. S. Lakshmanan, Jiawei Han, Alex Pang: Exploratory Mining and Pruning Optimizations of Constrained Association Rules. SIGMOD Conference 1998: 13-24 BibTeX

[RR96]

...

[SBMU98]

Craig Silverstein, Sergey Brin, Rajeev Motwani, Jeffrey D. Ullman: Scalable Techniques for Mining Causal Structures. VLDB 1998: 594-605 BibTeX

[TN98]

...

[Tuk77]

...

Referenced by

1. Themistoklis Palpanas: Knowledge Discovery in Data Warehouses. SIGMOD Record 29(3): 88-100(2000)
2. Sridhar Ramaswamy, Rajeev Rastogi, Kyuseok Shim: Efficient Algorithms for Mining Outliers from Large Data Sets. SIGMOD Conference 2000: 427-438
3. Markus M. Breunig, Hans-Peter Kriegel, Raymond T. Ng, Jörg Sander: LOF: Identifying Density-Based Local Outliers. SIGMOD Conference 2000: 93-104