Sentence examples for mean clustering accuracy from inspiring English sources
The mean clustering accuracy was calculated as: (7) CA ‾ n = 1 40 × 100 ∑ j = 1 40 ∑ k = 1 100 CA n (TC j, SC k ) CA ‾ is the mean CA and n is the number of tests used.
Table 4 Performance comparison of clustering analysis Algorithm name Iteration times Accuracy rate Time (ms) Traditional K-means cluster 1250 77.78 553 Modified K- means cluster 1250 88.89 4288.89
By minimizing the relationships between training and validation groups using K-means clustering, the accuracy of DGV ranged from 0.22 to 0.69, with an average 0.44 across 16 economically important traits.
Comparative analysis with other standard parallel version algorithms like Adaptive Parallel Particle Swarm Optimization (PPSO), Real Coded Parallel Genetic Algorithm (RCPGA) and K-means reveals the superior clustering accuracy of the proposed method.
Similarly, we attribute the improvement using multi-k-means method to its increased clustering accuracy resulting in better detection of the subtle effects of shared local similarity on the global metric.
We have shown the efficacy of an (mathcal{{M}} ( phi,p,mathcal{{F}} ))-based k-means clustering algorithm over the (l_{2} -based k-means cl_{2} -basedgorithm on the basis of better clustering accuracy obtained for a two-moon data set and path-based data set.
In Figure 3(a), it is shown that the clustering accuracy of the k-means clustering algorithm is 78% over the two-moon data set, whereas the clustering accuracy of our modefied algorithm k-means clustering is 84% (Figure 3(b)).
It means that if we consider only one start, we only lose 7% of clustering accuracy.
A feature selection framework is developed in order to improve the clustering accuracy and reduce computational complexity.1 Several clustering methods such as K-Means, Affinity Propagation, Mean Shift and Spectral Clustering were applied.
The clustering accuracy obtained by our proposed clustering algoritm outperformes the standard k-means clustering algorithm.
