2026-09-24T10:47:08Zhttps://bura.brunel.ac.uk/oai/request

oai:bura.brunel.ac.uk:2438/58272016-01-27T09:08:30Zcom_2438_23com_2438_58com_2438_8627com_2438_8620col_2438_3672col_2438_210col_2438_8629col_2438_235

Mladenović, N Alguwaizani, Abdulrahman 2011-09-20T08:21:22Z 2011-09-20T08:21:22Z 2011 http://bura.brunel.ac.uk/handle/2438/5827 This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University. Although there has been a rapid development of technology and increase of computation speeds, most of the real-world optimization problems still cannot be solved in a reasonable time. Some times it is impossible for them to be optimally solved, as there are many instances of real problems which cannot be addressed by computers at their present speed. In such cases, the heuristic approach can be used. Heuristic research has been used by many researchers to supply this need. It gives a sufficient solution in reasonable time. The clustering problem is one example of this, formed in many applications. In this thesis, I suggest a Variable Neighbourhood Search (VNS) to improve a recent clustering local search called K-Harmonic Means (KHM).Many experiments are presented to show the strength of my code compared with some algorithms from the literature. Some counter-examples are introduced to show that KHM may degenerate entirely, in either one or more runs. Furthermore, it degenerates and then stops in some familiar datasets, which significantly affects the final solution. Hence, I present a removing degeneracy code for KHM. I also apply VNS to improve the code of KHM after removing the evidence of degeneracy. Submitted by Otue Ekong (lbsrooe@brunel.ac.uk) on 2011-09-20T08:21:05Z No. of bitstreams: 1 FulltextThesis.pdf: 522971 bytes, checksum: 38130a2d090414725b17b5be694911cb (MD5) Approved for entry into archive by Otue Ekong(lbsrooe@brunel.ac.uk) on 2011-09-20T08:21:22Z (GMT) No. of bitstreams: 1 FulltextThesis.pdf: 522971 bytes, checksum: 38130a2d090414725b17b5be694911cb (MD5) Made available in DSpace on 2011-09-20T08:21:22Z (GMT). No. of bitstreams: 1 FulltextThesis.pdf: 522971 bytes, checksum: 38130a2d090414725b17b5be694911cb (MD5) en Brunel University, School of Information Systems, Computing and Mathematics School of Information Systems, Computing and Mathematics http://bura.brunel.ac.uk/bitstream/2438/5827/1/FulltextThesis.pdf Variable neighbourhood search based heuristic for K-harmonic means clustering Thesis