Linkclue: A matlab package for link-based cluster ensembles

Journal of Statistical Software

Volumn 36, Issue 9, 2010, Pages 1-36

  Iam On, Natthakan ^a   Garrett, Simon ^a  

^a ABERYSTWYTH UNIVERSITY   (United Kingdom)

Author keywords

Cluster ensembles; Cluster relation; Clustering; Link analysis; MATLAB; Pairwise similarity matrix

Indexed keywords

EID: 77958118466     PISSN: 15487660     EISSN: 15487660     Source Type: Journal    
DOI: 10.18637/jss.v036.i09     Document Type: Article

References (60)

1. Armstrong SA, Staunton JE, Silverman LB, Pieters R, den Boer ML, Minden MD, Sallan SE, Lander ES, Golub TR, Korsmeyer SJ (2002). MLL Translocations Apecify a Distinct Gene Expression Profile that Distinguishes a Unique Leukemia. Nature Genetics, 30(1), 41-47.
2. Ayad H, Kamel M (2003). Finding Natural Clusters Using Multi-Clusterer Combiner Based on Shared Nearest Neighbors. In Proceedings of International Workshop on Multiple Classifier Systems, pp. 166-175. Springer-Verlag, Berlin.
3. Boulis C, Ostendorf M (2004). Combining Multiple Clustering Systems. In Proceedings of European Conference on Principles and Practice of Knowledge Discovery in Databases, pp. 63-74. Springer-Verlag, New York.
4. Calado P, Cristo M, Gonçalves MA, de Moura ES, Ribeiro-Neto BA, Ziviani N (2006). LinkBased Similarity Measures for the Classification of Web Documents. Journal of American Society for Information Science and Technology, 57(2), 208-221.
5. Campello R (2007). A Fuzzy Extension of the Rand Index and Other Related Indexes for Clustering and Classification Assessment. Pattern Recognition Letters, 28(7), 833-841.
6. Cristofor D, Simovici D (2002). Finding Median Partitions Using Information-Theoretical- Based Genetic Algorithms. Journal of Universal Computer Science, 8(2), 153-172.
7. Davies DL, Bouldin DW (1979). A Cluster Separation Measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1(2), 224-227.
8. de Souto MC, Costa IG, de Araujo DSA, Ludermir TB, Schliep A (2008). Clustering Cancer Gene Expression Data: A Comparative Study. BMC Bioinformatics, 9, 497.
9. Denvijver PA, Kittler J (1982). Pattern Recognition: A Statictical Approach. Prentice Hall, Englewood Cliffs.
10. Domeniconi C, Al-Razgan M (2009). Weighted Cluster Ensembles: Methods and Analysis. ACM Transactions on Knowledge Discovery from Data, 2(4), 1-40.
11. Draper NR, Smith H (1998). Applied Regression Analysis. 3rd edition. John Wiley & Sons, New York.
12. Duda RO, Hart PE, Stork DG (2000). Pattern Classification. 2nd edition. John Wiley & Sons, New York.
13. Dudoit S, Fridyand J (2003). Bagging to Improve the Accuracy of a Clustering Procedure. Bioinformatics, 19(9), 1090-1099.
14. Dunn JC (1974). Well Separated Clusters and Optimal Fuzzy Partitions. Cybernetics and Systems, 4(1), 95-104.
15. Fern XZ, Brodley CE (2003). Random Projection for High Dimensional Data Clustering: A Cluster Ensemble Approach. In Proceedings of International Conference on Machine Learning, pp. 186-193. AAAI Press, Washington, DC.
16. Fern XZ, Brodley CE (2004). Solving Cluster Ensemble Problems by Bipartite Graph Partitioning. In Proceedings of International Conference on Machine Learning, pp. 36-43. ACM, New York.
17. Fischer B, Buhmann JM (2003). Bagging for Path-Based Clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(11), 1411-1415.
18. Fred ALN (2001). Finding Consistent Clusters in Data Partitions. In Proceedings of Sec- ond International Workshop on Multiple Classifier Systems, pp. 309-318. Springer-Verlag, London.
19. Fred ALN, Jain AK (2002). Data Clustering Using Evidence Accumulation. In Proceedings of International Conference on Pattern Recognition, pp. 276-280. IEEE Computer Society, Washington, DC.
20. Fred ALN, Jain AK (2003). Robust Data Clustering. In Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, pp. 128-136. IEEE Computer Society, Los Alamitos.
21. Fred ALN, Jain AK (2005). Combining Multiple Clusterings Using Evidence Accumulation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(6), 835-850.
22. Fred ALN, Jain AK (2006). Learning Pairwise Similarity for Data Clustering. In Proceedings of International Conference on Pattern Recognition, pp. 925-928. IEEE Computer Society, Washington, DC.
23. Gionis A, Mannila H, Tsaparas P (2005). Clustering Aggregation. In Proceedings of In- ternational Conference on Data Engineering, pp. 341-352. IEEE Computer Society, Los Alamitos.
24. Hadjitodorov ST, Kuncheva LI, Todorova LP (2006). Moderate Diversity for Better Cluster Ensembles. Information Fusion, 7(3), 264-275.
25. Hochbaum DS, Shmoys DB (1985). A Best Possible Heuristic for the k-Center Problem. Mathematics of Operations Research, 10(2), 180-184.
26. Hornik K (2005). A CLUE for CLUster Ensembles. Journal of Statistical Software, 14(12), 1-25 URL http://www.jstatsoft.org/v14/i12/.
27. Hu X, Yoo I (2004). Cluster Ensemble and its Applications in Gene Expression Analysis. In Proceedings of Asia-Pacific Bioinformatics Conference, pp. 297-302. Australian Computer Society, Darlinghurst.
28. Huang Z (1998). Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values. Data Mining Knowledge Discovery, 2, 283-304.
29. Hubert L, Arabie P (1985). Comparing Partitions. Journal of Classification, 2, 193-218.
30. Iam-on N, Boongoen T, Garrett S (2008). Refining Pairwise Similarity Matrix for Cluster Ensemble Problem with Cluster Relations. In Proceedings of Eleventh International Conference on Discovery Science, pp. 222-233. Springer-Verlag, Berlin.
31. Iam-on N, Boongoen T, Garrett S (2010). LCE: A Link-Based Cluster Ensemble Method for Improved Gene Expression Data Analysis. Bioinformatics, 26(12), 1513-1519.
32. Jain AK, Dubes RC (1998). Algorithms for Clustering Data. Prentice-Hall, New Jersey.
33. Jain AK, Law MHC (2005). Data Clustering: A User's Dilemma. In Proceedings of International Conference on Pattern Recognition and Machine Intelligence, pp. 1-10. Springer- Verlag, Berlin.
34. Jain AK, Murty MN, Flynn PJ (1999). Data Clustering: A Review. ACM Computing Survey, 31(3), 264-323.
35. Jeh G, Widom J (2002). SimRank: A Measure of Structural-Context Similarity. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 538-543. ACM, New York.
36. Karypis G, Aggarwal R, Kumar V, Shekhar S (1999). Multilevel Hypergraph Partitioning: Applications in VLSI Domain. IEEE Transaction on VLSI System, 7(1), 69-79.
37. Karypis G, Kumar V (1998). Multilevel k-Way Partitioning Scheme for Irregular Graphs. Journal of Parallel Distributed Computing, 48(1), 96-129.
38. Kaufman L, Rousseeuw PJ (1990). Finding Groups in Data: An Introduction to Cluster Analysis. John Wiley & Sons, New York.
39. Kittler J, Hatef M, Duin R, Matas J (1998). On Combining Classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(3), 226-239.
40. Klink S, Reuther P, Weber A, Walter B, Ley M (2006). Analysing Social Networks within Bibliographical Data. In Proceedings of International Conference on Database and Expert Systems Applications, pp. 234-243. Springer-Verlag, Berlin.
41. Kuncheva LI, Hadjitodorov ST (2004). Using Diversity in Cluster Ensembles. In Proceedings of the IEEE International Conference on Systems, Man & Cybernetics, pp. 1214-1219. IEEE Computer Society, Washington, DC.
42. Kuncheva LI, Vetrov D (2006). Evaluation of Stability of k-Means Cluster Ensembles with Respect to Random Initialization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(11), 1798-1808.
43. Kyrgyzov IO, Maitre H, Campedel M (2007). A Method of Clustering Combination Applied to Satellite Image Analysis. In Proceedings of International Conference on Image Analysis and Processing, pp. 81-86. IEEE Computer Society, Washington, DC.
44. Law M, Topchy A, Jain AK (2004). Multiobjective Data Clustering. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, volume 2, pp. 424-430. IEEE Computer Society, Los Alamitos.
45. Luszczek P (2008). Enhancing Multicore System Performance Using Parallel Computing with MATLAB. MATLAB Digest, 17(5). URL http://www.mathworks.com/company/newsletters/digest/2008/sept/parallel-computing.html.
46. Minaei-Bidgoli B, Topchy A, Punch W (2004). A Comparison of Resampling Methods for Clustering Ensembles. In Proceedings of the International Conference on Machine Learning: Models, Technologies and Applications, pp. 939-945. CSREA Press.
47. Moler C (2007).Parallel MATLAB: Multiple Processors and Multiple Cores. The MathWorks News and Notes. URL http://www.mathworks.com/company/newsletters/news_notes/june07/clevescorner.html.
48. Monti S, Tamayo P, Mesirov JP, Golub TR (2003). Consensus Clustering: A ResamplingBased Method for Class Discovery and Visualization of Gene Expression Microarray Data. Machine Learning, 52(1-2), 91-118.
49. Ng A, Jordan M, Weiss Y (2001). On Spectral Clustering: Analysis and an Algorithm. Advances in Neural Information Processing Systems, 14, 849-856.
50. Nguyen N, Caruana R (2007). Consensus Clusterings. In Proceedings of IEEE International Conference on Data Mining, pp. 607-612. IEEE Computer Society, Washington, DC.
51. Rand WM (1971). Objective Criteria for the Evaluation of Clustering Methods. Journal of the American Statistical Association, 66, 846-850.
52. Strehl A, Ghosh J (2002). Cluster Ensembles: A Knowledge Reuse Framework for Combining Multiple Partitions. Journal of Machine Learning Research, 3, 583-617.
53. Swift S, Tucker A, Vinciotti V, Martin N, Orengo C, Liu X, Kellam P (2004). Consensus Clustering and Functional Interpretation of Gene-Expression Data. Genome Biology, 5, R94.
54. The MathWorks, Inc (2007). MATLAB - The Language of Technical Computing, Version 7.5. The MathWorks, Inc., Natick, Massachusetts. URL http://www.mathworks.com/products/matlab/
55. Topchy AP, Jain AK, Punch WF (2003). Combining Multiple Weak Clusterings. In Proceedings of IEEE International Conference on Data Mining, pp. 331-338. IEEE Computer Society, Washington, DC.
56. Topchy AP, Jain AK, Punch WF (2004). A Mixture Model for Clustering Ensembles. In Proceedings of SIAM International Conference on Data Mining, pp. 379-390. SIAM, Philadelphia.
57. Topchy AP, Jain AK, Punch WF (2005). Clustering Ensembles: Models of Consensus and Weak Partitions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(12),1866-1881.
58. Wolpert DH, Macready WG (1995). No Free Lunch Theorems for Search. Technical Report SFI-TR-95-02-010, Santa Fe Institute.
59. Xue H, Chen S, Yang Q (2009). Discriminatively Regularized Least-Squares Classification. Pattern Recognition, 42(1), 93-104.
60. Yu Z, Wong HS, Wang H (2007). Graph-Based Consensus Clustering for Class Discovery from Gene Expression Data. Bioinformatics, 23(21), 2888-2896.