Dear all, I am trying to do some cluster analysis, both with the base R and the apcluster. Both methods give 2 clusters, which is what I am looking for since I am interested in identifying positive and negative results. However I could not find a way to fine-tuning the analysis in order to properly allocate the points; essentially the negative points should be all those in the lower left portion of the plot (see example) but some in the top centre are also given to the negative cluster. So how can I change the parameters to get better results? Thank you L
>>> x <- c(3.15, 3.07, 2, 3, 2.97, 45, 3.21, 45, 40.55, 2, 22.09, 2.47, 2.97, 2.77, 2.6, 7.35, 4.11, 37.12, 2.73, 36.36, 45, 2.33, 2.49, 45, 2.4, 2.74, 2.64, 45, 2.47, 38.1, 2.47, 37.4, 2.77, 2.37, 45, 2.69, 2.97, 2.7, 2, 2, 2.55, 11.86, 2.51, 2.68, 2.31, 2.6, 2.45, 2, 2.72, 2.57, 2.09, 3.04, 45, 45, 2.13, 43.82, 2.92, 4.94, 24.82, 2.64, 4.96, 3.65, 2.67, 2.64, 8.04, 4.56, 44.87, 37.42, 45, 6.2, 2.84, 4.08, 2, 5.03, 2.27, 44.89, 2.41, 2.47, 2.78, 37.47, 45, 2.76, 45, 2.51, 2.8, 44.8, 6.2, 2.87, 2.23, 18.32, 3.14, 2.1, 2.38, 2.72, 2, 2, 44.41, 3.15, 3.06, 4.8, 2.77, 2.8, 2.71, 44.77, 2.25, 2.69, 28.38, 2, 2.95, 45, 2.79, 2.46, 2.61, 2.78, 2.94, 38.47, 3.29, 2.89, 2.4, 2.23, 2.62, 4.21, 2.61, 2.81, 2.41, 41.98, 2.39, 36.41, 44.84, 4.73, 2, 2.66, 4.57, 3.01, 42.64, 2.04, 5.49, 15.48, 3.08, 2.7, 2, 2, 2.09, 2, 2.29, 2.92, 3.39, 3.1, 2, 6.14, 7.03, 4.77, 2.55, 32.36, 20.61, 3.09, 4.46, 44.75, 2, 2.73, 2, 36.05, 3.61, 34.84, 2.69, 5.28, 3.04, 45, 2.47, 2.58, 2.16, 2.59, 45, 44.08, 2, 37.05, 2.48, 2.46, 38.71, 7.32, 2.95, 2.8, 44.58, 42.24, 36.99, 13.84, 45, 2, 2, 2.38, 45, 45, 43.59, 2.69, 2.81, 3.05, 2.8, 4.65, 45, 41.46, 2.33, 7.12, 19.18, 4.82, 4.76, 2.51, 3.1, 2.74, 4.99, 38.06, 2.53, 2.94, 2.93, 6.59, 2.72, 2.94, 2.56, 2.91, 44.79, 2.98, 42.95, 45, 2.63, 38.44, 2.71, 2, 37.92, 2.69, 2.91, 2.65, 44.48, 6.35, 2.56, 21.94, 3.08, 2.6, 45, 2, 2.62, 2.47, 2.62, 2.73, 2.87, 2.83, 4.56, 44.22, 5.15, 5.13, 2.76, 7.02, 28.61, 4.87, 5.02, 44.35, 2.26, 2.89, 5.26, 38.01, 44.79, 39.26, 2.91, 4.59, 2.69, 2.61, 34.97, 3, 45, 2.81, 2, 2.65, 2, 37.33, 4.69, 3.26, 38.24, 4.97, 4.62, 2.47, 45, 4.52, 2.73, 15.66, 6.06, 2.79, 2.87, 45, 45, 45, 4.84, 3.05, 4.89, 4.64, 4.92, 2.74, 7.83, 42.31, 2.88, 6.89, 23.06, 2.94, 4.72, 4.55, 5.52, 4.48, 4.86, 3.12, 7.68, 43.89, 2.82, 2.64, 3.05, 42.95, 2.33, 3.55, 45, 2.79, 2.47, 45, 2.56, 38.33, 2.73, 2.87, 2.61, 3.01, 2.86, 2.74, 44.46, 44.54, 2.62, 16.94, 2.53, 2.24, 2.72, 2, 3.1, 2.88, 7.4, 4.64, 8.25, 3.01, 2.86, 2.46, 5.67, 44.52, 2.47, 2, 29.01, 2.61, 3.23, 12.3, 3.9, 2.91, 43.99, 36.99, 43.72, 42.29, 2.63, 3.03, 2.85, 2.58, 2.63, 2.73, 2.57, 2.37, 2.57, 2.75, 44.14, 39.4, 40.02, 3.08, 45, 4.96, 3, 2.83, 2.74, 2.8, 2.8, 18.88, 4.69, 2.51, 4.32, 2, 2.56, 2.81 ) y <- c(0.014, 0.04, 0.001, 0.023, 0.008, 0, 0.008, 0.001, -0.001, 0.002, 0.103, 0, 0.013, 0.005, 0.008, 0.001, 0.011, 0.076, 0.005, 0.045, -0.001, 0, 0.008, -0.002, 0.002, 0.016, 0.006, 0.001, 0.002, 0.001, 0.004, 0.086, 0.009, 0.011, 0.002, 0.013, 0.019, 0.007, 0, 0.002, 0.024, 0.119, 0.015, 0.009, 0.013, 0.017, 0.009, 0.009, 0.006, 0.012, 0.002, 0.015, 0, 0.001, 0.002, 0.001, 0.007, 0.004, 0.113, 0.016, 0.013, 0.004, 0.015, 0.005, 0.004, 0.007, 0, 0.081, 0.001, 0.002, 0.014, 0.002, 0, 0.01, 0.003, 0.002, 0.004, 0.004, 0.006, 0.064, 0, 0.014, 0, 0.01, 0.019, 0.002, 0.006, 0.005, 0.003, 0.103, 0.007, 0.008, 0.002, 0.013, 0.007, 0.004, 0.001, 0.04, 0.017, 0.018, 0.002, 0.006, 0.011, 0.003, 0.004, 0.008, 0.115, 0, 0.02, 0, 0.012, 0.009, 0.011, 0.013, 0.004, 0.058, 0.019, 0.006, 0.005, 0.004, 0.012, 0.003, 0.003, 0.004, 0.002, 0.001, 0.002, 0.102, -0.001, 0.008, 0.002, 0.016, 0.023, 0.014, 0.053, 0.009, 0.001, 0.124, 0.009, 0.008, 0.002, 0.002, 0.013, 0.002, 0.001, 0.042, 0.011, 0.009, 0, 0.004, 0.003, 0.002, 0.005, 0, 0.101, 0.013, 0.009, 0.005, 0.002, 0.007, 0.008, 0.067, 0.002, 0.064, 0.028, 0.007, 0.006, 0, 0.007, 0.006, 0, 0.001, 0.001, 0.001, 0, 0.088, 0.005, 0.008, 0.098, 0.005, 0.019, 0.007, 0.05, -0.002, 0.002, 0.129, 0.001, 0.004, -0.001, 0.002, -0.001, 0, 0.043, 0.018, 0.019, 0.015, 0.003, 0.006, 0.002, 0.001, 0.002, 0.004, 0.097, 0.025, 0.022, 0.007, 0.011, 0.007, 0.013, 0.061, 0.008, 0.013, 0.028, 0.004, 0.013, 0.005, 0.01, 0.004, 0, 0.006, -0.001, 0.001, 0.01, 0.061, 0.002, 0.004, 0, 0.011, 0.029, 0.018, 0, 0.003, 0.012, 0.085, 0.015, 0.007, 0.002, 0.003, 0.008, 0.002, 0.007, 0.02, 0.011, 0.02, 0.008, 0.001, 0.003, 0.01, 0.014, 0.001, 0.096, 0.027, 0.024, 0, 0.005, 0.006, 0.024, 0.087, 0.001, 0.083, 0.02, 0.009, 0.009, 0.001, 0, 0.019, 0, 0.003, -0.001, 0.002, 0, 0.089, 0.016, 0.01, 0.103, 0.003, 0.01, 0.002, 0.008, 0.005, 0.014, 0.1, 0.007, 0.009, 0.011, -0.001, 0, 0.002, 0.015, 0.036, 0.018, 0.026, 0.009, 0.008, 0.004, 0.001, 0.014, 0.009, 0.1, 0.026, 0.032, 0.008, 0.011, 0.004, 0.013, 0.019, 0.004, 0.02, 0.015, 0.005, 0.013, -0.001, 0.013, 0.012, 0, 0.01, 0.002, 0.001, 0.013, 0.066, 0.009, 0.005, 0.002, 0.013, 0.025, 0.006, 0, 0, 0.015, 0.121, 0.006, 0.003, 0.008, 0, 0.012, 0.011, 0.003, 0.022, 0.008, 0.032, 0.007, 0.002, 0.006, 0.007, 0, 0.003, 0.11, 0.01, 0.008, 0, 0.018, 0.008, 0.001, 0.087, 0, 0.028, 0.011, 0.014, 0.007, 0.001, 0.018, 0.033, 0.021, 0.003, 0.003, 0.007, -0.001, 0.07, 0.022, 0.009, 0.001, 0.007, 0.031, 0.008, 0.013, 0.01, 0.018, 0.125, 0.01, 0.015, 0.006, 0, 0.015, 0.019 ) z <- cbind(x, y) k <- kmeans(z, 2) plot(z, col=k$cluster) library(apcluster) m <- apclusterK(negDistMat(r=2), z, K=2, verbose=TRUE) plot(m, z) ______________________________________________ R-help@r-project.org mailing 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