Fuzzy clustering is a well-established technique among the well-known clustering techniques in several real-world applications due to easy implementation and produces satisfactory clustering result. However, it has so...
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Fuzzy clustering is a well-established technique among the well-known clustering techniques in several real-world applications due to easy implementation and produces satisfactory clustering result. However, it has some deficiency such as sensitive to outliers, result dependency on choosing initial centroid, etc. To eradicate the shortcoming of FCM algorithm, this article introduces a robust clustering technique, particle swarm optimization improved fuzzy c-means is developed by the hybridization of particle swarm optimization and improved fuzzy c-means techniques, to deal with noisy data and initialization problem. In this article, a fuzzy clustering technique is developed to increase the convergence performance of clustering techniques. Fuzzy c-means is improved by developing a new metric to tolerate the noisy environment. Particle swarm optimization has an inbuilt guidance strategy which leads the solution in particle swarm optimization to obtain useful information from the better solution and thereby helping them improve their own solution. To handle the initialization problem of fuzzy c-means, particle swarm optimization technique is used. PSO effectively enhance the performance of improved FCM to increase the effectiveness of clustering. The effectiveness of the proposed clustering technique over existing techniques in literature has been illustrated by adopting eight real worlds and three artificial data sets. The results show that the proposed algorithm generates encouraging results as compared to the established clustering technique in literature.
Purpose To identify sources of exposure variability for the tumor growth inhibitor 17-dimethylaminoethylamino-17-demethoxygeldanamycin (17-DMAG) using a population pharmacokinetic analysis. Methods A total 67 solid tu...
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Purpose To identify sources of exposure variability for the tumor growth inhibitor 17-dimethylaminoethylamino-17-demethoxygeldanamycin (17-DMAG) using a population pharmacokinetic analysis. Methods A total 67 solid tumor patients at 2 centers were given 1 h infusions of 17-DMAG either as a single dose, daily for 3 days, or daily for 5 days. Blood samples were extensively collected and 17-DMAG plasma concentrations were measured by liquid chromatography/mass spectrometry. Population pharmacokinetic analysis of the 17-DMAG plasma concentration with time was performed using nonlinear mixed effect modeling to evaluate the effects of covariates, inter-individual variability, and between-occasion variability on model parameters using a stepwise forward addition then backward elimination modeling approach. The inter-individual exposure variability and the effects of between-occasion variability on exposure were assessed by simulating the 95 % prediction interval of the AUC per dose, AUC(0-24) h, using the final model and a model with no between-occasion variability, respectively, subject to the five day 17-DMAG infusion protocol with administrations of the median observed dose. Results A 3-compartment model with first order elimination (ADVAN11, TRANS4) and a proportional residual error, exponentiated inter-individual variability and between occasion variability on Q2 and V1 best described the 17-DMAG concentration data. No covariates were statistically significant. The simulated 95% prediction interval of the AUC(0-24) h for the median dose of 36 mg/m(2) was 1,059-9,007 mg/L h and the simulated 95 % prediction interval of the AUC(0-24) h considering the impact of between-occasion variability alone was 2,910-4,077 mg/L h. Conclusions Population pharmacokinetic analysis of 17-DMAG found no significant covariate effects and considerable inter-individual variability;this implies a wide range of exposures in the population and which may affect treatment outcome. Patients treat
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