An approximating polynomial algorithm for a sequence partitioning problem

A. V. Kel’manov and A. V. Pyatkin, “Complexity of Certain Problems of Searching for Subsets of Vectors and Cluster Analysis,” Zh. Vychisl. Mat. Mat. Fiz. 49(11), 2059–2067 (2009) [Comput. Math. Math. Phys. 49 (11), 1966–1971 (2009)].

A. V. Kel’manov and A. V. Pyatkin, “On Complexity of Some Problems of Cluster Analysis of Vector Sequences,” Diskretn.Anal. Issled. Oper. 20(2), 47–57 (2013) [J. Appl. Indust. Math. 7 (3), 363–369 (2013)].

A. V. Kel’manov and S. M. Romanchenko, “An Approximation Algorithm for Solving a Problem of Search for a Vector Subset,” Diskretn. Anal. Issled. Oper. 18(1), 61–69 (2011) [J. Appl. Indust. Math. 6 (1), 90–96 (2012)].

A. V. Kel’manov and S. A. Khamidullin, “Posteriori Detection of a Given Number of Identical Subsequences in a Quasiperiodic Sequence,” Zh. Vychisl. Mat. Mat. Fiz. 41(5), 807–820 (2001) [Comput. Math. Math. Physics. 41 (5), 762–774 (2001)].

D. Aloise, A. Deshpande, P. Hansen, and P. Popat, “NP-Hardness of Euclidean Sum-of-Squares Clustering,” G-2008-33 (Les Cahiers du GERAD, 2008).

K. Anil and K. Jain, “Data Clustering: 50 Years Beyond k-Means,” Pattern Recognit. Lett. 31, 651–666 (2010).

M. R. Garey and D. S. Johnson, Computers and Intractability: a Guide to the Theory of NP-Completeness (Freeman, San Francisco, 1979).

T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Springer, New York, 2001).

A. V. Kel’manov and B. Jeon, “A Posteriori Joint Detection and Discrimination of Pulses in a Quasiperiodic Pulse Train,” IEEE Trans. Signal Process. 52(3), 645–656 (2004).