SPARCL: An effective and efficient algorithm for mining arbitrary shape-based clusters

Knowledge and Information Systems

Volumn 21, Issue 2, 2009, Pages 201-229

  Chaoji, Vineet ^a   Al Hasan, Mohammad ^a   Salem, Saeed ^a   Zaki, Mohammed J ^a  

^a RENSSELAER POLYTECHNIC INSTITUTE   (United States)

Author keywords

Clustering; Hierarchical; Kmeans; Linear time; Spatial

Indexed keywords

DATA MINING; HIERARCHICAL CLUSTERING; ITERATIVE METHODS; LARGE DATASET;

CLUSTERING; HIERARCHICAL; K-MEANS; LINEAR TIME; SPATIAL;

CLUSTERING ALGORITHMS;

EID: 70350536514     PISSN: 02191377     EISSN: 02193116     Source Type: Journal    
DOI: 10.1007/s10115-009-0216-0     Document Type: Article

References (45)

1. Astrahan MM (1970) Speech analysis by clustering, or the hyperphoneme method. Stanford A.I. Project Memo, Stanford University
2. Aupetit M (2003) Robust topology representing networks. In: Proceedings of the European symposium on artificial neural networks, Bruges, Belgium, pp 45-50
3. Arthur D, Vassilvitskii S (2007) k-means++: The advantages of careful seeding. In: Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, New Orleans, Louisiana, 2007, pp 1027-1035
4. Ball GH, Hall DJ (1967) PROMENADE - an online pattern recognition system. Stanford Research Institute, Stanford University
5. Bradley PS, Fayyad UM (1998) Refining initial points for k -means clustering. In: Proceedings of the fifteenth international conference on machine learning Madison, Wisconsin, pp 91-99
6. Breunig MM, Kriegel H, Ng RT, Sander J (2000) LOF: Identifying density-based local outliers. In: Proceedings of 2000 ACM-SIGMOD international conference on management of data, Dallas, Texas, 2000, pp 93-104
7. Chazelle B, Palios L (1994) Decomposition algorithms in geometry. In: Bajaj C (eds) Algebraic geometry and its applications. Springer, Berlin, pp 419-447
8. Cormen TH, Leiserson CE, Rivest RL, Stein C (2005) Introduction to algorithms, 2nd edn. MIT Press, Cambridge
9. Dasgupta S, Schulman LJ (2000) A two-round variant of EM for Gaussian mixtures. In: Proceedings of the 16th conference on uncertainty in artificial intelligence, Stanford, CA, June 2000, pp 152-159
10. Dhillon IS, Guan Y, Julis B (2004) Kernel k-means, spectral clustering and normalized cuts. In: Proceedings of the tenth international conference on knowledge discovery and data mining, Seattle, WA, August 2004, pp 551-556
11. Domeniconi C, Gunopulos D (2004) An efficient density-based approach for data mining tasks. Knowl Inform Syst 6(6): 750-770
12. Duda RO, Hart PE, Stork DG (2000) Pattern classification, 2nd edn. Wiley-Interscience, New York
13. Ester M, Kriegel H-P, Sander J, Xu X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of ACM SIGKDD international conference on knowledge discovery and data mining, 1996, pp 226-231
14. García JA, Fdez-Valdivia J, Cortijo FJ, Molina R (1995) A dynamic approach for clustering data. Signal Process 44(2):181-196
15. Gao BJ, Ester M, Cai J-Y, Schulte O, Xiong H (2007) The minimum consistent subset cover problem and its applications in data mining. In: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining San Jose, California, USA, 2007, pp 310-319
16. Guha S, Rastogi R, Shim K (1998) CURE: An efficient clustering algorithm for large databases. In: ACM SIGMOD international conference on management of data, Seattle, WA, pp 73-84
17. Hasan MA, Chaoji V, Salem S, Zaki MJ (2008) Robust partitional clustering by outlier and density insensitive seeding. Technical Report 08-04, RPI Computer Science
18. Hinneburg A, Gabriel H-H (2007) DENCLUE 2.0: Fast clustering based on kernel density estimation. In: International symposium on intelligent data analysis, pp 70-80
19. Hinneburg A, Keim DA (2003) A general approach to clustering in large databases with noise. Knowl Inform Syst 5(4):387-415
20. Hinneburg A, Keim DA (1998) An efficient approach to clustering in multimedia databases with noise. In: Proceedings of 4th international conference on knowledge discovery and data mining. AAAI Press, New York
21. Hu X, Pan Y (2007) Knowledge discovery in bioinformatics: Techniques, methods, and applications (Wiley Series in Bioinformatics). Wiley, New York
22. Jain AK, Dubes RC (1988) Algorithms for clustering data. Prentice-Hall, Upper Saddle River
23. Klaus J (2003) Clustering intrusion detection alarms to support root cause analysis. ACM Trans Inform Syst Secur 6(4):443-471
24. Karypis G, Han E-H, Kumar V (1999) Chameleon: Hierarchical clustering using dynamic modeling. Computer 32(8):68-75
25. Katsavounidis I, Kuo CCJ, Zhen Z (1994) A new initialization technique for generalized Lloyd iteration. IEEE Signal Process Lett 1(10):144-146
26. Kaufman, L Rousseeuw, PJ (1990) Finding groups in data: An introduction to cluster analysis. Wiley Series in Probability and Mathematical Statistics. Applied Probability and Statistics, New York
27. Koga H, Ishibashi T, Watanabe T (2007) Fast agglomerative hierarchical clustering algorithm using Locality-Sensitive Hashing. Knowl Inform Syst 12(1):25-53
28. Lee J, Verleysen M (2007) Nonlinear dimensionality reduction. Springer, Berlin
29. Martinetz T, Schulten K (1994) Topology representing networks. Neural Netw 7(3):507-522
30. Meila M, Shi J (2001) A random walks view of spectral segmentation. AI and Statistics (AISTATS)
31. Miller HJ, Han J (2001) Geographic data mining and knowledge discovery. Taylor & Francis, Bristol
32. Ng AY, Jordan M, Weiss Y (2001) On spectral clustering: Analysis and an algorithm. In: Advances in neural information processing systems, vol 14
33. Okabe A, Boots B, Sugihara K (1992) Spatial tessellations: Concepts and applications of voronoi diagrams. Wiley, New York
34. Punj G, Stewart DW (1983) Cluster analysis in marketing research: Review and suggestions for application. J Mark Res 20(2):134-148
35. Sack JR, Urrutia J (2000) Handbook of computational geometry. North-Holland, Amsterdam
36. Shawe-Taylor J, Cristianini N (2004) Kernel methods for pattern analysis. Cambridge University Press, Cambridge
37. Sheikholeslami G, Chatterjee S, Zhang A (1998) WaveCluster: A multi-resolution clustering approach for very large spatial databases. In: Proceedings of 24th international conference on very large data bases, pp 428-439
38. Shi J, Malik J (2000) Normalized cuts and image segmentation. IEEE Trans Pattern Anal Mach Intell 22(8):888-905
39. Tenenbaum JB, de Silva V, Langford JC (2000) A global geometric framework for nonlinear dimensionality reduction. Science 290(5500):2319-2323
40. Vennam JR, Vadapalli S (2005) SynDECA: A tool to generate synthetic datasets for evaluation of clustering algorithms. In: 11th international conference on management of data
41. Wu X, Kumar V, Ross Q, Ghosh J, Yang Q, Motoda H, McLachlan GJ, Ng A, Liu B, Yu PS, Zhou Z-H, Steinbach M, Hand DJ, Steinberg D (2008) Top 10 algorithms in data mining. Knowl Inform Syst 14(1):1-37
42. Zelnik-Manor L, Perona P (2004) Self-tuning spectral clustering. In: Proceedings of 18th annual conference on neural information processing systems
43. Zeng H-J, He Q-C, Chen Z, Ma W-Y, Ma J (2004) Learning to cluster web search results. In: Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval, New York, NY, pp 210-217
44. Zhang T, Ramakrishnan R, Livny M (1997) BIRCH: A new data clustering algorithm and its applications. Data Min Knowl Discov 1(2):141-182
45. Zhao Y, Karypis G (2005) Hierarchical clustering algorithms for document datasets. Data Min Knowl Discov 10(2):141-168