## 4 Results and Discussion
### Discussions  
_uCLUST_ algorithm is checked with the following comparative parameters: (1)Homogenity--defined as all of the clusters obtained, as a result of clustering that contain only data points which are members belonging to a single class, then the clustering algorithm is said  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Attribute** & **No. of** & **Model No.** & & **uCLUST—** & **Time taken(secs)** \\ \cline{3-6}
**Used for** & **Clusters** & **(No. of** & **Clusters)** & **Test Set** & **Clustering** & **Training (_ELM++_)** & **Incre.** \\ \cline{3-6}  & **Clustering** & **Formed** & **(Clusters)** & **(Clusters)** & **(Clusters)** & **Test** \\ \hline \multirow{3}{*}{format} & \multirow{3}{*}{2} & \(M1(1)\) & \multirow{3}{*}{1,2} & \multirow{3}{*}{9} & 16.45 & \multirow{3}{*}{8.86} \\ \cline{3-6}  & & & \(M2(1)\) & & & 15.9 & \\ \hline \multirow{3}{*}{image\_type} & \multirow{3}{*}{4} & \(M1(2)\) & \multirow{3}{*}{1,2,3,4} & \multirow{3}{*}{13} & 12.78 & \multirow{3}{*}{6.35} \\ \cline{3-6}  & & \(M2(1)\) & & & 11.89 & \\ \cline{3-6}  & & \(M3(1)\) & & & 12.05 & \\ \hline \multirow{3}{*}{image\_type, format} & \multirow{3}{*}{8} & \(M1(4)\) & \multirow{3}{*}{1,2,3,4,5,} & \multirow{3}{*}{22} & 12.35 & \multirow{3}{*}{6.11} \\ \cline{3-6}  & & \(M2(2)\) & & & 11.34 & \\ \cline{3-6}  & & \(M3(2)\) & & & 11.96 & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance Time of _CUIL_.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Set No.** & **S1** & **S2** & **S3** & **S4** & **S5** & **S6** \\ \hline
**d** & 8 & 2.75 & 0.1 & 2.07 & 3.4 & 0.7 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Statistical Test.  
to be _homogeneous_, (2) Completeness--defined as if all the data points of a given class belongs to the same cluster as a result of clustering, then the clustering algorithm is said to be _complete_, and (3) V-measure--defined as the harmonic mean between homogenity and completeness, calculated using \(V=2*(homogenity*completeness)/(homogenity+completeness)\). The values got for our model are, (1) _homogenity_(_uCLUST_) = 0.98, (2) _completeness(uCLUST_) = 0.99_ and (3) _V-measure_(_uCLUST_) = 0.985.  
The performance of _uCLUST_ is compared with the clustering algorithm _FDCA_ (Fast Density Clustering Algorithm) [32] for clustering unlabeled MNIST handwriting number images. _FDCA_ uses symmetric complex wavelet transformation to evaluate the similarity among the unlabaled number images and segregate them as clusters. For MNIST dataset, _FDCA_ gives a performance accuracy of 89.8% while _uCLUST_ gives a clustering accuracy of 99%. The advantage of _uCLUST_ over _FDCA_ is the use of unstructured JSON metadata for representing the images, while _FDCA_ uses complex wavelet functions which leads to complexity and more computational cost.  
The key facts of using incremental algorithm _ELM++_ are, (1) the feedforward neural network ELM requires less time to train a model even in case of large number of hidden neurons when compared to other neural network algorithms like backpropagation, as ELM does not undergo any iterations for finding the trained weights. ELM is resistant to overfitting, except in case of very small datasets. As long as there is enough memory space to calculations, ELM provides good performance accuracy as the training sample increases and (2) since our experiments deal with very large dataset and to overcome the memory requirement of ELM, we have proposed and tested the incremental learning algorithm _ELM++_ which trains each batch of arriving data independently to create individual models, which avoids the retraining of previous batch data and remembers the previously learned information. To classify the test images, these generated models are combined overcoming the memory space requirement.