4 Ebola Collection Malaysia Airlines Collection Run LDA Clustering LDA Clustering 1 0.0472 0.4293 0.117 0.601 2 0.0475 0.4240 0.140 0.635 3 0.0486 0.4329 0.144 0.632 4 0.0483 0.4374 0.149 0.629 5 0.0501 0.4344 0.133 0.622 6 0.0475 0.3957 0.151 0.621 7 0.0491 0.4257 0.162 0.618 8 0.0466 0.4653 0.133 0.618 9 0.0472 0.4091 0.149 0.634 10 0.0483 0.4397 0.137 0.597 The significant level (α) in our experiment is set to 0.05 (95% confidence interval). We use Minitab, which is statistical analysis tool, to compute t-test. We can reject the null hypothesis and accept the alternative hypothesis if p-value is less than the significant level. The result for the Ebola collection is shown as follows: Two-sample T for LDA vs Clustering N Mean StDev SE Mean LDA 10 0.04804 0.00105 0.00033 Clustering 10 0.4294 0.0185 0.0059 Difference = mu (LDA) - mu (Clustering) Estimate for difference: -0.38131 95% upper bound for difference: -0.37056 T-Test of difference = 0 (vs <): T-Value = -65.01 P-Value = 0.000 DF = 9 For the Ebola collection, we do not have enough evidence to accept the null hypothesis. Therefore, we accept the alternative hypothesis, which means LDA has better performance than the clustering method because the average cosine similarity of LDA is lower than the average cosine similarity of the clustering approach. The result for the Malaysia Airlines collection is shown as follows:
4 Ebola Collection Malaysia Airlines Collection Run LDA Clustering LDA Clustering 1 0.0472 0.4293 0.117 0.601 2 0.0475 0.4240 0.140 0.635 3 0.0486 0.4329 0.144 0.632 4 0.0483 0.4374 0.149 0.629 5 0.0501 0.4344 0.133 0.622 6 0.0475 0.3957 0.151 0.621 7 0.0491 0.4257 0.162 0.618 8 0.0466 0.4653 0.133 0.618 9 0.0472 0.4091 0.149 0.634 10 0.0483 0.4397 0.137 0.597 The significant level (α) in our experiment is set to 0.05 (95% confidence interval). We use Minitab, which is statistical analysis tool, to compute t-test. We can reject the null hypothesis and accept the alternative hypothesis if p-value is less than the significant level. The result for the Ebola collection is shown as follows: Two-sample T for LDA vs Clustering N Mean StDev SE Mean LDA 10 0.04804 0.00105 0.00033 Clustering 10 0.4294 0.0185 0.0059 Difference = mu (LDA) - mu (Clustering) Estimate for difference: -0.38131 95% upper bound for difference: -0.37056 T-Test of difference = 0 (vs <): T-Value = -65.01 P-Value = 0.000 DF = 9 For the Ebola collection, we do not have enough evidence to accept the null hypothesis. Therefore, we accept the alternative hypothesis, which means LDA has better performance than the clustering method because the average cosine similarity of LDA is lower than the average cosine similarity of the clustering approach. The result for the Malaysia Airlines collection is shown as follows: