Research Article
Spatial Cluster Analysis by the Bin-Packing Problem and DNA Computing Technique
(a) generate: Generate multiple copies of all the combinations as . Append | as the position numbers of . Then append and to store the energies. | (b) energy: Compute the dissimilarities of the possible clusters and store in energy. | (c) find: Find the best solution. | (d) count: Count the number of clusters. | Now we present algorithms to implement the above procedures. | (a) Generation of all the possible solutions. Append values in order to store the energies. | generate | | for to do | for to | | endfor | for to | | endfor | for to | | endfor | for downto | | endfor | endfor | | . | for to | | endfor | . | (b) Energy computation. The problem is to compute totals of energy for those where . | Hence and . The total energy is stored in . At the same time, | the counting number of each bin is stored in the following stickers. | energy | for to do | | endfor | for to do | for to do | | | | if yes then | | endif | endfor | endfor | for to do | for to do | for to do | | endfor | endfor | | | endfor | (c) The next step is to find the best solution with least energy. If yes in the final step, | then we get the optimal solution. The final number of clusters in stored in the last sticker. | find | , | for to do | , | if no then | | else | | endif | endfor | . | (d) The final step is to count the number of clusters. It is stored in the last sticker while in the variable . | count | | for to do | | if yes then | | | endif | endfor |
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