TI-DBSCAN: Clustering with DBSCAN by means of the triangle inequality

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6086 LNAI, Issue , 2010, Pages 60-69

  Kryszkiewicz, Marzena ^a   Lasek, Piotr ^a  

^a WARSAW UNIVERSITY OF TECHNOLOGY   (Poland)

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTERING DATA; DATA MINING TASKS; DATA POINTS; DENSITY-BASED ALGORITHM; HIGH DIMENSIONAL DATA; HIGH QUALITY; NEIGHBORHOOD SEARCH; SPATIAL ACCESS METHODS; SPATIAL INDICES; SPEED-UPS; THREE ORDERS OF MAGNITUDE; TRIANGLE INEQUALITY;

DATA MINING; ROUGH SET THEORY;

CLUSTERING ALGORITHMS;

EID: 79956257766     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-13529-3_8     Document Type: Conference Paper

References (10)

1. Beckmann, N., Kriegel, H.P.: The R*-tree: An Efficient and Robust Access Method for Points and Rectangles. In: Proc. of ACM SIGMOD, Atlantic City, pp. 322-331 (1990)
2. Elkan, C.: Using the Triangle Inequality to Accelerate k-Means. In: Proc. of ICML 2003, Washington, pp. 147-153 (2003)
3. Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A Density-Based Algorithm for Discovering Clusters in Large Spatial Database with Noise. In: Proc. of KDD 1996, Portland, pp. 226- 231 (1996)
4. Guttman, A.: R-Trees: A Dynamic Index Structure For Spatial Searching. In: Proc. Of ACM SIGMOD, Boston, pp. 47-57 (1984) (Pubitemid 15469534)
5. Kryszkiewicz, M., Lasek, P.: TI-DBSCAN: Clustering with DBSCAN by Means of the Triangle Inequality, ICS Research Report, Warsaw University of Technology (April 2010)
6. Kryszkiewicz, M., Skonieczny, L.: Faster Clustering with DBSCAN. In: Proc. of IIPWM 2005, Gdańsk, pp. 605-614 (2005)
7. Moore, A.W.: The Anchors Hierarchy: Using the Triangle Inequality to Survive High Dimensional Data. In: Proc. of UAI, Stanford, pp. 397-405 (2000)
8. Stonebraker, M., Frew, J., Gardels, K., Meredith, J.: The SEQUOIA 2000 Storage Benchmark. In: Proc. of ACM SIGMOD, Washington, pp. 2-11 (1993)
9. Zhang, T., Ramakrishnan, R., Livny, M.: BIRCH: A New Data Clustering Algorithm and its Applications. Data Mining and Knowledge Discovery 1(2), 141-182 (1997)
10. http://kdd.ics.uci.edu/databases/kddcup98/kddcup98.html