Review seriation and matrix reordering methods: An historical overview

Statistical Analysis and Data Mining

Volumn 3, Issue 2, 2010, Pages 70-91

  Liiv, Innar ^a  

^a TALLINN UNIVERSITY OF TECHNOLOGY   (Estonia)

Author keywords

Bertin's matrices; Biclustering; Co clustering; Combinatorial data analysis; Matrix reordering; Seriation

Indexed keywords

CELLULAR MANUFACTURING; DATA HANDLING; INDUSTRIAL RESEARCH; INFORMATION ANALYSIS; INFORMATION SYSTEMS; MAPS; OPERATIONS RESEARCH;

BERTIN MATRIX; BICLUSTERING; CO-CLUSTERING; COMBINATORIAL DATA ANALYSIS; DATA ANALYSIS TECHNIQUES; MATRIX; MATRIX REORDERING; ONE-DIMENSIONAL; PATTERN DISCOVERY; SERIATION;

MATRIX ALGEBRA;

EID: 77952175261     PISSN: 19321872     EISSN: 19321864     Source Type: Journal    
DOI: 10.1002/sam.10071     Document Type: Review

References (171)

1. B. B. Bederson and B. Shneiderman, The Craft of Information Visualization: Readings and Reflections, San Francisco, Morgan Kaufmann, 2003.
2. B. Shneiderman, Inventing discovery tools: combining information visualization with data mining, Inf Vis 1 (2002), 5-12.
3. H. Späth, Cluster Analysis Algorithms for Data Reduction and Classification of Objects, Chichester, UK, Ellis Horwood, 1980.
4. F. Murtagh, Book review: W. Gaul and M. Schader, Eds., Data, expert knowledge and decisions, Heidelberg: Springer-Verlag, 1988, viii + 380, J Classification 6 (1989), 129-132.
5. P. Arabie and L. J. Hubert, An overview of combinatorial data analysis, In Clustering and Classification, P. Arabie, L. J. Hubert, and G. De Soete, eds. River Edge, World Scientific, 1996, 5-63.
6. P. Arabie and L. J. Hubert, Combinatorial data analysis, Annu Rev Psychol 43 (1992), 169-203.
7. J. Bertin, Graphics and Graphic Information Processing, Berlin, Walter de Gruyter (Translated by W. J. Berg and P. Scott), 1981.
8. G. Gruvaeus and H. Wainer, Two additions to hierarchical cluster analysis, Br J Math Stat Psychol 25 (1972), 200-206.
9. Z. Bar-Joseph, D. K. Gifford, and T. S. Jaakkola, Fast optimal leaf ordering for hierarchical clustering, Bioinformatics 17(S1) (2001), S22-S29.
10. Z. Bar-Joseph, E. D. Demaine, D. K. Gifford, N. Srebro, A. M. Hamel, and T. S. Jaakkola, K-ary clustering with optimal leaf ordering for gene expression data, Bioinformatics 19(9) (2003), 1070-1078.
11. P.Arabie,L.J.Hubert,andS. Schleutermann, Blockmodels from the bond energy approach, Soc Networks 12 (1990), 99-126.
12. J. R. King and V. Nakornchai, Machine-component group formation in group technology: review and extension, Int J Prod Res 20(2) (1982), 117-133.
13. J. F. Goncalves and M. G. C. Resende, An evolutionary algorithm for manufacturing cell formation, Comput Ind Eng 47(2-3) (2004), 247-273.
14. M. J. O'Brien and L. R. Lyman, Seriation, Stratigraphy, and Index Fossils: The Backbone of Archaeological Dating, New York, Kluwer Academic/Plenum Publishers 1999.
15. W. H. Marquardt, Advances in archaeological seriation, In Advances in Archaeological Method and Theory, M. B. Schiffer, ed. New York, Academic Press, 1978, 257-314.
16. L. R. Tucker, The extension of factor analysis to three-dimensional matrices, Contributions to Mathematical Psychology, New York, Holt, Rinehart and Winston, 1964, 110-127.
17. P. D. Grünwald, The Minimum Description Length Principle, Cambridge, MIT Press, 2007.
18. J. Rissanen, Modelling by shortest data description, Automatica 14 (1978), 465-471.
19. C. Faloutsos and V. Megalooikonomou, On data mining, compression, and Kolmogorov, Data Min Knowl Discov 15(1) (2007), 3-20.
20. I. S. Dhillon, S. Mallela, and D. S. Modha, "Information-Theoretic Co-Clustering," In Proceedings of The Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining(KDD-2003), Washington, DC, USA, August 24-27, 2003, 89-98.
21. I. Liiv, Pattern Discovery Using Seriation and Matrix Reordering: A Unified View, Extensions and an Application to Inventory Management. Ph.D. Thesis, Tallinn University of Technology, 2008.
22. L. Wilkinson, The Grammar of Graphics, (2nd ed.), New York, Springer-Verlag, 2005.
23. D. J. Carroll and P. Arabie, Multidimensional scaling, Annu Rev Psychol 31 (1980), 607-649.
24. E. Forsyth and L. Katz, A matrix approach to the analysis of sociometric data: preliminary report, Sociometry 9(4) (1946), 340-347.
25. M. Ghoniem, J. Fekete, P. Castagliola, On the readability of graphs using node-link and matrix-based representations: a controlled experiment and statistical analysis, Inf Vis 4(2) (2005), 114-135.
26. N. Henry, J. Fekete, and M. J. McGuffin, NodeTrix: a hybrid visualization of social networks, IEEE Trans Vis Comput Graph 13(6) (2007), 1302-1309.
27. J. Czekanowski, Zur differentialdiagnose der Neandertalgruppe, Korrespondenzblatt Deutsch Ges Anthropol Ethnol Urgesch XL(6/7) (1909), 44-47.
28. J. Bertin, Sémiologie graphique: les diagrammes, les réseaux, les cartes, Paris, Mouton, 1967.
29. W. T. McCormick, P. J. Schweitzer, and T. W. White, Problem decomposition and data reorganization by a clustering technique, Oper Res 20(5) (1972), 993-1009.
30. W. T. McCormick, S. B. Deutsch, J. J. Martin, and P. J. Schweitzer, Identification of Data Structures and Relationships by Matrix Reordering Techniques (TR P-512), Arlington, Institute for Defense Analyses, 1969.
31. J. E. Mullat, Extremal subsystems of monotonic systems I, Automation Remote Control 37 (1976), 758-766.
32. J. E. Mullat, Extremal subsystems of monotonic systems II, Automation Remote Control 37 (1976), 1286-1294.
33. J. E. Mullat, Extremal subsystems of monotonic systems III, Automation Remote Control 38 (1977), 89-96.
34. L. Vyhandu, Fast methods in exploratory data analysis, Trans Tallinn Univ Technol 705 (1989), 3-13.
35. L. Vyhandu, Some methods to order objects and variables in data systems (in Russian), Trans Tallinn Univ Technol 482 (1980), 43-50.
36. I. Van Mechelen, H.-H. Bock, and P. De Boeck, Two-mode clustering methods: a structured overview, Stat Methods Med Res 13 (2004), 363-394.
37. B. G. Mirkin, Mathematical Classification and Clustering (Nonconvex Optimization and Its Applications), Boston-Dordrecht, Kluwer Academic Press, 1996.
38. F. W. M. Petrie, Sequences in prehistoric remains, J Anthropol Inst Great Britain and England 29(3/4) (1899), 295-301.
39. L. R. Lyman, S. Wolverton, and M. J. O'Brien, Seriation, superposition, and interdigitation: a history of Americanist graphic depictions of culture change, Am Antiq 63(2) (1998), 239-261.
40. D. G. Kendall, Seriation from abundance matrices, In Mathematics in the Archaeological and Historical Sciences, F. R. Hodson, D. G. Kendall, and P. Tautu, eds. Chicago, Aldine-Atherton, 1971, 214-252.
41. F. Hole and M. Shaw, Computer Analysis of Chronological Seriation, Houston, Rice University Studies, 1967.
42. P. Ihm, "A contribution to the history of seriation in archaeology," in Proc. Classification-the Ubiquitous Challenge, Proceedings of the 28th Annual Conference of the Gesellschaft für Klassifikation e.V., University of Dortmund, March 911, 2004 2005, 307-316.
43. L. R. Lyman, M. J. O'Brien, and R. C. Dunnell, The Rise and Fall of Culture History, New York, Plenum Press, 1997.
44. G. W. Brainerd, The place of chronological ordering in archaeological analysis, Am Antiq 16(4) (1951), 301-313.
45. W. S. Robinson, A method for chronologically ordering archaeological deposits, Am Antiq 16(4) (1951), 293-301.
46. M. Ascher and R. Ascher, Chronological ordering by computer, Am Anthropol 65(5) (1963), 1045-1052.
47. R. S. Kuzara, G. R. Mead, and K. A. Dixon, Seriation of anthropological data: a computer program for matrix-ordering, Am Anthropol 68(6) (1966), 1442-1455.
48. J. H. Rowe, Stratigraphy and seriation, Am Antiq 26(3) (1961), 324-330.
49. R. C. Dunnell, Seriation method and its evaluation, Am Antiq 35(3) (1970), 305-319.
50. D. J. Lehmer, Robinson's coefficient of agreement. A critique, Am Antiq 17(2) (1951), 151-151.
51. P. Dempsey and M. Baumhoff, The statistical use of artifact distributions to establish chronological sequence, Am Antiq 28(4) (1963), 496-509.
52. D. G. Kendall, Incidence matrices, interval graphs and seriation in archaeology, Pacific J Math 28(3) (1969), 565-570.
53. D. G. Kendall, Some problems and methods in statistical archaeology, World Archaeol 1(1) (1969), 68-76.
54. F. R. Hodson, D.G. Kendall, P. Tautu, eds. Mathematics in the Archaeological and Historical Sciences, Chicago, Aldine-Atherton, 1971.
55. A. C. Spaulding, Some elements of quantitative archaeology, In Mathematics in the Archaeological and Historical Sciences, F. R. Hodson, D. G. Kendall, and P. Tautu, eds. Chicago, Aldine-Atherton, 1971, 3-16.
56. W. Skrzywan, Metoda grupowania na podstawie tablic prof. Czekanowskiego, Przegl Anthropol 18 (1952), 583-599.
57. F. A. Szczotka, On a method of ordering and clustering of objects, Zastosow Mat 13 (1972), 23-34.
58. W. Pluta, Sravnitel'nyi mnogomernyi analiz v ekonomicheskih issledovanijah (in Russian), Moskva, Statistika, 1980
59. A. Soltysiak and P. Jaskulski, Czekanowski's diagram. A method of multidimensional clustering, In Proceedings of the New Techniques for Old Times. CAA 98. Computer Applications and Quantitative Methods in Archaeology. Proceedings of the 26th Conference, Barcelona, Spain, March 25-28, 1998, 1999, 175-184.
60. R. L. Carneiro, Scale analysis as an instrument for the study of cultural evolution, Southwest J Anthropol 18(2) (1962), 149-169.
61. V. Elisseeff, Possibilités du scalogramme dans l'étude des bronzes chinois arhaiques, Math Sci Humaines 11 (1965), 1-10.
62. R. Rao and S. K. Card, "TheTableLens:Merging Graphical and Symbolic Representations in an Interactive Focus+Context Visualization for Tabular Information," in Proc. Proceedings of the ACM SIGCHI, Boston, MA, United States, April 24-28, 1994 1994, 318-322.
63. M. Friendly and E. Kwan, Effect ordering for data displays, Comput Stat Data Anal 43 (2003), 509-539.
64. M. Friendly, Corrgrams: exploratory displays for correlation matrices, Am Stat 56 (2002), 316-324.
65. H. Siirtola, Interaction with the reorderable matrix, In Proceedings of the International Conference on Information Visualization (IV'99), London, July 14-16, 1999, 272-277
66. H. Siirtola, Interactive cluster analysis, In Proceedings of the 8th International Conference on Information Visualisation (IV'04), London, UK, July 14-16, 2004, 471-476.
67. H. Siirtola and E. Mäkinen, Constructing and reconstructing the reorderable matrix, Inf Vis 4(1) (2005), 32-48.
68. E. Mäkinen and H. Siirtola, Reordering the reorderable matrix as an algorithmic problem, In Proceedings of The Theory and Application of Diagrams: First International Conference, Diagrams 2000, Edinburgh, Scotland, UK, September 1-3, 2000, 453-467.
69. E. Mäkinen and H. Siirtola, The barycenter heuristic and the reorderable matrix, Informatica 29(3) (2005), 357-363.
70. H. Siirtola, Combining parallel coordinates with the reorderable matrix, In Proceedings of the conference on Coordinated and Multiple Views In Exploratory Visualization, London, July 16-18, 2003, 63-74.
71. C. Mueller, B. Martin, and A. Lumsdaine. Interpreting large visual similarity matrices, In Proceedings of Asia-Pacific Symposium on Visualization, Sydney, Australia, 2007, 149-152.
72. C. Mueller, B. Martin, and A. Lumsdaine. A comparison of vertex ordering algorithms for large graph visualization, In Proceedings of Asia-Pacific Symposium on Visualization 2007, Sydney, Australia, 141-148.
73. R. F. Ling, A computer generated aid for cluster analysis, Commun ACM 16(6) (1973), 355-361.
74. M. W. Berry, B. Hendrickson and P. Raghavan, Sparse matrix reordering schemes for browsing hypertext, In The Mathematics of Numerical Analysis, Vol. 32, Lectures in Applied Mathematics, J. Renegar, M. Shub, and S. Smale, eds. AMS, Park City, UT, 1996, 99-123.
75. C.-H. Chen, W. Härdle, and A. Unwin, Handbooks of Computational Statistics: Data Visualization, Heidelberg, Springer-Verlag 2008.
76. C.-H. Chen, Generalized association plots: Information visualization via iteratively generated correlation matrices, Stat Sinica 12 (2002), 7-29.
77. C.-H. Chen, H.-G. Hwu, W.-J. Jang, C.-H. Kao, Y.-J. Tien, S. Tzeng, and H.-M. Wu, Matrix visualization and information mining, In Proceedings in Computational Statistics 2004 (Compstat 2004), Prague, Czech Republic, Heidelberg, August 23-27, 2004, 85-100.
78. L. Wilkinson, M. Friendly, The history of the cluster heat map, Am Stat 63(2) (2009), 179-184.
79. J. L. Moreno, Who Shall Survive? A New Approach to the Problem of Human Inter-relations, New York, Beacon House, 1934.
80. J. L. Moreno, Who Shall Survive? Foundations of Sociometry, Group Psychotherapy and Sociodrama, New York, Beacon House, 1953.
81. L. Katz, On the matric analysis of sociometric data, Sociometry 10(3) (1947), 233-241.
82. H. Jennings, Structure of leadership-development and sphere of influence, Sociometry 1(1/2) (1937), 99-143.
83. S. C. Dodd, The interrelation matrix, Sociometry 3(1) (1940), 91-101.
84. S. Wasserman and K. Faust, Social Network Analysis, Cambridge, Cambridge University Press 1994.
85. C. O. Beum and E. G. Brundage, A Method for Analyzing the Sociomatrix, Sociometry 13(2) (1950), 141-145.
86. J. Moody, Peer influence groups: identifying dense clusters in large networks, Soc Networks 23 (2001), 261-283.
87. J. L. Moreno, Sociogram and Sociomatrix: a note to the paper by Forsyth and Katz, Sociometry 9(4) (1946), 348-349.
88. E. F. Borgatta and W. Stolz, A note on a computer program for rearrangement of matrices, Sociometry 26(3) (1963), 391-392.
89. W. de Nooy, A. Mrvar, and V. Batagelj, Exploratory Social Network Analysis with Pajek. Structural Analysis in the Social Sciences, Cambridge University Press, New York, NY, 2005.
90. F. Murtagh, Multidimensional Clustering Algorithms, Vienna, Physika Verlag, 1985.
91. H. White, S. A. Boorman, and R. Breiger, Social structure from multiple networks. I. Blockmodels of roles and positions, Am J Econ Sociol 81 (1976), 730-790.
92. L. J. Hubert, Problems of seriation using a subject by item response matrix, Psychol Bull 81(12) (1974), 976-983.
93. L. J. Hubert, Seriation using asymmetric proximity measures, Br J Math Stat Psychol 29 (1976), 32-52.
94. C. H. Coombs, A Theory of Data, New York, John Wiley and Sons, 1964.
95. L. J. Hubert, P. Arabie, and J. Meulman, Combinatorial Data Analysis: Optimization by Dynamic Programming, Philadelphia, Society for Industrial and Applied Mathematics, 2001.
96. M. Brusco and S. Stahl, Branch-and-Bound Applications in Combinatorial Data Analysis (Statistics and Computing), New York, Springer, 2005.
97. S. Kulczynski, Die pflanzenassoziation der pieninen, Bull Int Acad Polon Sci Lett: Class Sci Math Nat Ser., B 2 (1927), 57-203.
98. P. Legendre and L. Legendre, Numerical ecology, Amsterdam, Elsevier Science, 1998.
99. Ø. Hammer and D. Harper, Paleontological Data Analysis, Oxford, Blackwell Publishing, 2005.
100. Ø. Hammer, PAST-PAlaeontological STatistics. 2009. http://folk.uio.no/ohammer/past/ (Retrieved October 1, 2009).
101. K. Clarke and R. Warwick, Change in Marine Communities: An Approach to Statistical Analysis and Interpretation (2nd ed.), Plymouth, UK, Primer-E, 2001.
102. I. Miklos, I. Somodi, and J. Podani, Rearrangement of ecological data matrices via Markov Chain Monte Carlo simulation, Ecology 86(12) (2005), 3398-3410.
103. H. Mannila, Finding total and partial orders from data for seriation, Discovery Sci 5255 (2008), 16-25.
104. G. Garriga, E. Junttila, and H. Mannila, Banded structure in binary matrices, In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2008), Las Vegas, Nevada, August 24-27, 2008, 292-300.
105. R. R. Sokal and P. H. A. Sneath, Principles of Numerical Taxonomy, San Francisco, W. H. Freeman, 1963.
106. J. A. Hartigan, Direct Clustering of a Data Matrix, J Am Stat Assoc 67(337) (1972), 123-129.
107. J. A. Hartigan, Clustering Algorithms, New York, John Wiley and Sons 1975.
108. Y. Cheng and G. M. Church, "Biclustering of expression data," in Proc. Proceedings of the 8th International Conference on Intelligent Systems for Molecular Biology, La Jolla / San Diego, CA, USA, August 19-23, 2000, 2000, 93-103.
109. B. G. Mirkin and S. N. Rodin, Graphs and Genes, Berlin, Springer-Verlag 1984.
110. M. B. Eisen, P. T. Spellman, P. O. Brown, and D. Botstein, Cluster analysis and display of genome-wide expression patterns, Proc Natl Acad Sci USA 95(25) (1998), 14863-14868.
111. S. C. Madeira and A. L. Oliveira, Biclustering algorithms for biological data analysis: a survey, IEEE Trans Comput Biol Bioinform 1(1) (2004), 24-45.
112. A. Tanay, R. Sharan, and R. Shamir, Biclustering algorithms: a survey, Handbook of Computation Molecular Biology, Boca Raton, CRC Press, 2006, 26.1-26.17.
113. A. Prelic, S. Bleuler, P. Zimmermann, A. Wille, P. Bühlmann, W. Gruissem, L. Hennig, L. Thiele, and E. Zitzler, A systematic comparison and evaluation of biclustering methods for gene expression data, Bioinformatics 22(9) (2006), 1122-1129.
114. R. M. Haralick, The diclique representation and decomposition of binary relations, J ACM 21(3) (1974), 356-366.
115. R. Wille, Concept lattices and conceptual knowledge systems, Comput Math Appl 23 (1992), 493-515.
116. B. Ganter and R. Wille, Formal Concept Analysis. Mathematical Foundations, Berlin, Springer, 1999.
117. N. Gale and W. C. Halperin, Unclassed matrix shading and optimal ordering in hierarchical cluster analysis, J Classification 1 (1984), 75-92.
118. D. L. Vandev and Y. G. Tsvetanova, Ordering of Hierarchical Classifications 1997.
119. D. L. Vandev and Y. G. Tsvetanova, Perfect chains and single linkage clustering algorithm, In Proceedings of Statistical Data Analysis (SDA-95), Varna, Bulgaria, September 23-28, 1995, 99-107.
120. G. Caraux and S. Pinloche, PermutMatrix: a graphical environment to arrange gene expression profiles in optimal linear order, Bioinformatics 21(7) (2005), 1280-1281.
121. G. Caraux, Reorganisation et représentation visuelle d'une matrice de donnée numériques: un algorithme itératif, Rev Stat Appl 32(4) (1984), 5-23.
122. J. L. Burbidge, The new approach to production, Prod Eng 40(12) (1961), 769-784.
123. J. L. Burbidge, Production flow analysis, Prod Eng 42(12) (1963), 742-752.
124. J. McAuley, Machine grouping for efficient production, Prod Eng 51(2) (1972), 53-57.
125. J. L. Burbidge, Production flow analysis, Prod Eng 50(4) (1971), 139-152.
126. S. P. Mitrofanov, Nauchnye osnovy gruppovoj tehnologii (in Russian), Leningrad, Lenizdat, 1959.
127. S. P. Mitrofanov, The Scientific Principles of Group Technology, London, National Lending Library for Science and Technology, 1966.
128. J. L. Burbidge, A manual method for production flow analysis, Prod Eng 56 (1977), 34-38.
129. J. R. King, Machine-component group formation in production flow analysis: an approach using a rank order clustering algorithm, Int J Prod Res 18(2) (1980), 213-232.
130. M. P. Chandrasekharan and R. Rajagopalan, Groupability: an analysis of the properties of binary data for group technology, Int J Prod Res 27 (1989), 1035-1052.
131. K. R. Kumar and M. P. Chandrasekharan, Group efficacy: a quantitative criterion for goodness of block diagonal forms of binary matrices in group technology, Int J Prod Res 28(2) (1990), 233-243.
132. R. G. Askin and K. S. Chiu, A graph partitioning procedure for machine assignment and cell formation in group technology, Int J Prod Res 28(8) (1990), 1555-1572.
133. R. G. Askin, S. H. Cresswell, and J. B. Goldberg, A Hamiltonian path approach to reordering the part-machine matrix for cellular manufacturing, Int J Prod Res 29(6) (1991), 1081-1100.
134. R. Rajagopalan and J. L. Batra, Design of cellular production systems-a graph theoretic approach, Int J Prod Res 13(6) (1975), 567-579.
135. R. K. Gunasingh and R. S. Lashkari, Machine grouping problem in cellular manufacturing systems an integer programming approach, Int J Prod Res 27(9) (1989), 1465-1473.
136. C. H. Chu and J. C. Hayya, A fuzzy clustering approach to manufacturing cell formation, Int J Prod Res 29(7) (1991), 1475-1487.
137. H. Hwang and J. U. Sun, A genetic-algorithm-based heuristic for the GT cell formation problem, Comput Ind Eng 30(4) (1996), 941-955.
138. G. C. Onwubolo and M. Mutingi, A genetic algorithm approach to cellular manufacturing systems, Comput Ind Eng 39 (2001), 125-144.
139. J. Balakrishnan and P. D. Jog, Manufacturing cell formation using similarity coefficients and a parallel genetic TSP algorithm formulation and comparison, Math Comput Model 21(12) (1995), 61-73.
140. A Kusiak and Y. K. Chung, GT/ART: using neural network to form machine cells, Manuf Rev 4(4) (1991), 293-301
141. S. Kaparthi and N. C. Suresh, Machine-component cell formation in group technology: a neural network approach, Int J Prod Res 30(6) (1992).
142. H. A. Rao and P. Gu, Design of cellular manufacturing systems: a neural network approach, Int J Control Autom Syst: Res Eng 2(4) (1993), 407-424.
143. C. H. Chu, An improved neural network for manufacturing cell formation, Decision Support Syst 20 (1997), 279-295.
144. A. Kusiak and C. H. Cheng, A branch-and-bound algorithm for solving the group technology problem, Ann Oper Res 26 (1990), 415-431.
145. A. Kusiak, Branching algorithms for solving the group technology problem, J Manuf Syst 10(4) (1991), 332-343.
146. A. Kusiak, J. W. Boe, and C. H. Cheng, Designing cellular manufacturing systems: branch-and-bound and A* approaches, IEE Trans 25(4) (1993), 46-56.
147. V. Venugopal and T. T. Narendran, Cell formation in manufacturing systems through simulated annealing: an experimental evaluation, Eur J Oper Res 63 (1992), 409-422.
148. Y.-T. Park and U. Wemmerlöv, A shop structure generator for cell formation research, Int J Prod Res 32(10) (1994), 2345-2360.
149. F. Marcotorchino, Block seriation problems: A unified approach. Reply to the problem of H. Garcia and J. M. Proth (Applied Stochastic Models and Data Analysis, 1(1) (1985), 25-34)', Appl Stoch Models Data Anal 3(2) (1987), 73-91.
150. F. Marcotorchino, Seriation problems: an overview, Appl Stoch Models Data Anal 7(2) (1991), 139-151.
151. U. Wemmerlöv and N. L. Hyer, Cellular manufacturing in the U.S. industry: a survey of users, Int J Prod Res 27(9) (1989), 1511-1530.
152. N. Singh, Design of cellular manufacturing systems: an invited review, Eur J Oper Res 69 (1993), 284-291.
153. C. T. Mosier, J. Yelle, and G. Walker, Survey of similarity coefficient based methods as applied to the group technology configuration problem, Omega 25(1) (1997), 65-79.
154. C. H. Chu and M. Tsai, A comparison of three array-based clustering techniques for manufacturing cell formation, Int J Prod Res 28(8) (1990), 1417-1433.
155. J. Miltenburg and W. Zhang, A comparative evaluation of nine well-known algorithms for solving the cell formation problem in group technology, J Oper Manage 10(1) (1991), 44-72.
156. L. Kandiller, A comparative study of cell formation in cellular manufacturing systems, Int J Prod Res 32(10) (1994), 2395-2429.
157. U. Wemmerlöv and N. L. Hyer, Research issues in cellular manufacturing, Int J Prod Res 25(3) (1987), 413-431.
158. J. K. Lenstra, Clustering a data array and the traveling-salesman problem, Oper Res 22(2) (1974), 413-414.
159. S. Climer and W. Zhang, Rearrangement clustering: pitfalls, remedies, and applications, J Mach Learn Res 7 (2006), 919-943.
160. P. Arabie and L. J. Hubert, The bond energy algorithm revisited, IEEE Trans Syst Man Cybern 20(1) (1990), 268-274.
161. S. B. Deutsch and J. J. Martin, An ordering algorithm for analysis of data arrays, Oper Res 19(6) (1971), 1350-1362
162. L. Vyhandu, Fast methods for data processing (in Russian), Computer Systems: Computer Methods for Revealing Regularities, Novosibirsk, Institute of Mathematics Press, 1981, 20-29.
163. L. Vyhandu, Rapid data analysis methods (in Russian), Trans Tallinn Univ Technol 464 (1979), 21-39.
164. I. Liiv, R. Kuusik, and L. Vyhandu, Conformity analysis with structured query language, In Proceedings of the 6th International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases, Corfu Island, Greece, February 16-19, 2007, 187-189.
165. A. Saar and R. Kuusik, Metod vydeleniya yader v trjohmernom predstavlenii sociologicheskih dannyh (in Russian), Opyt Izucheniya Teleradio-Zhurnalistiki i Obshhestvennogo Mneniya, Tallinn, Periodika, 1987, 175-189.
166. R. Kuusik, Metod determinacionnogo analiza v sluchae trjohmernogo predstavleniya sovokupnosti dannyh (in Russian), Opyt Izucheniya Teleradio-Zhurnalistiki i Obshhestvennogo Mneniya, Tallinn, Periodika, 1987, 208-221.
167. R. Kuusik and A. Saar, Ob opyte primeneniya metoda vydeleniya yader v trjohmernom predstavljenii sociologicheskih dannyh (in Russian), Opyt Izucheniya Teleradio-Zhurnalistiki i Obshhestvennogo Mneniya, Tallinn, Periodika, 1987, 191-207.
168. B. G. Mirkin and I. Muchnik, Clustering and multidimensional scaling in Russia (1960-1990): a review, In Clustering and Classification, P. Arabie, L. J. Hubert, and G. De Soete, eds. River Edge, World Scientific 1996, 295-339.
169. E. Tyugu, Algorithms and Architectures of Artificial Intelligence, Amsterdam, IOS Press 2007.
170. I. Liiv, A. Vedeshin, and E. Täks, Visualization and structure analysis of legislative acts: a case study on the law of obligations, In Proceedings of the 11th ACM International Conference on Artificial Intelligence and Law (ICAIL), Proceedings of the Conference, Stanford University, June 4-8, 2007, 189-190.
171. I. Liiv, Visualization and data mining method for inventory classification, In Proceedings of The 2007 IEEE International Conference on Service Operations and Logistics, and Informatics, Philadelphia, August 27-29, 2007, 472-477.