Re-designing Distance Functions and Distance-Based Applications for High Dimensional Data

  title={Re-designing Distance Functions and Distance-Based Applications for High Dimensional Data},
  author={Charu C. Aggarwal},
  journal={SIGMOD Record},
In recent years, the detrimental effects of the curse of high dimensionality have been studied in great detail on several problems such as clustering, nearest neighbor search, and indexing. In high dimensional space the data becomes sparse, and traditional indexing and algorithmic techniques fail from the performance perspective. Recent research results show that in high dimensional space, the concept of proximity may not even be qualitatively meaningful [6]. In this paper, we try to outline… CONTINUE READING
Highly Cited
This paper has 137 citations. REVIEW CITATIONS

From This Paper

Topics from this paper.


Publications citing this paper.
Showing 1-10 of 73 extracted citations

137 Citations

Citations per Year
Semantic Scholar estimates that this publication has 137 citations based on the available data.

See our FAQ for additional information.


Publications referenced by this paper.
Showing 1-5 of 5 references

What is the nearest neighbor in high dimensional spaces

  • A. Hinneburg, C C.Aggarwal, D. Keim
  • VLDB Conference,
  • 2000
Highly Influential
5 Excerpts

Fast Algorithms for Projected Clustering

  • C CAggarwal
  • ACM SIGMOD Conference,
  • 1999
Highly Influential
5 Excerpts

Similar Papers

Loading similar papers…