data clustering

Nowadays, clustering methods have received much attention because the volume and variety of data are increasing considerably.The main problem of classical clustering methods is that they easily fall into local optima. Meta-heuristic algorithms have shown good results in data clustering. They can search the problem space to find appropriate cluster centers. One of these algorithms is gray optimization wolf (GWO) algorithm. The GWO algorithm shows a good exploitation and obtains good solutions in some problems, but its disadvantage is poor exploration. As a result, the algorithm converges to local optima in some problems. In this study, an improved version of gray optimization wolf (GWO) algorithm called 4-gray wolf optimization (4GWO) algorithm is proposed for data clustering. In 4GWO, the exploration capability of GWO is improved, using the best position of the fourth group of wolves called scout omega wolves. The movement of each wolf is calculated based on its score. The better score is closer to the best solution and vice versa. The performance of 4GWO algorithm for the data clustering (4GWO-C) is compared with GWO, particle swarm optimization (PSO), artificial bee colony (ABC), symbiotic organisms search (SOS) and salp swarm algorithm (SSA) on fourteen datasets. Also, the efficiency of 4GWO-C is compared with several various GWO algorithms on these datasets. The results show a significant improvement of the proposed algorithm compared with other algorithms. Also, EGWO as an Improved GWO has the second rank among the different versions of GWO algorithms. The average of F-measure obtained by 4GWO-C is 82.172%; while, PSO-C as the second best algorithm provides 78.284% on all datasets.

Among thorax tumors, lung tumors move mainly due to respiration. In order to enhance the precision of radiotherapy, one solution is estimating tumor motion from external motion of chest wall and abdomen regions. For this aim, consistent prediction models are constructed and then implemented for real time tumor motion tracking. In these models, clustering of database extracted from tumor motion and chest wall motion has non-negligible effect which has been taken into account in this work. In this investigation, motion database of fifteen patients with lung cancer who were treated by means of Cyberknife Synchrony system at Georgetown University hospital, has been used. Two subtractive and fuzzy C-means as common available clustering strategies have been employed in order to investigate their quantitative effects, in a comparative fashion. Final analyzed results show that the average targeting error of prediction models (difference between tumor position estimated by model and actual position of tumor) over all patients are 6.5 and 7.5 mm implementing subtractive and fuzzy C-means clustering, respectively. Moreover, using fuzzy C-means algorithm, tumor tracking is done with more stability. Since, breathing phenomena has high degree of variations, motion data clustering has an important role on the accuracy of prediction model performance by determining model parameters while constructing at pre-treatment step and while updating the model during the treatment.