## 3 The Proposed Algorithm
### Hybridization Proposed Algorithm with K-Means  
Many challenges appeared while utilizing real data in classification problems such as nonlinear classification, overlap among classes, and the presence of outliers; one of the clustering algorithms is used to make CCA algorithm more flexible to deal with such cases. This represents an advantage of an algorithm that is used in another algorithm to solve a particular problem or to increase its efficiency, using clustering with CCA in such a case as using hidden layers in Neural Networks NN, whereas in another simulation of NN, partition clustering gives different results with the same input dataset. The iteration is to be applied using K-means at a specific number and different clusters are obtained, whereas it depends on the initial random selection to select the center for such a process to lead to different Cores within these clusters in every iteration. Therefore, the model will be trained to attain higher accuracy. Both techniques are used in dealing with nonlinear classification and other problems.  
The structure of training dataset separation, representing 80% of all datasets, according to this study's scenario, has three classes confronting two clusters in each of them, for instance, Cor_c1 (k1) is the Core of cluster 1 within class 1 and Cor_c1 (k2) represents the Core of cluster 2 within class 1, and so on.  
Figure 3 shows a flowchart summarizing the general idea of Core Classify Algorithm (CCA), with three class scenarios divided into two clusters within every class; n represents the number of points in the testing dataset using j as a counter, and I as a counter of the iterations' times (ite) to train the model within every iteration process the accuracy and Cores to be stored. Finally, the Cores of clusters that have a high accuracy will be chosen as appropriate Cores of a perfect model.  
The strength and spread of any classification algorithm do not depend on its accuracy in ideal cases such as linear or discriminative classification, but on the extent to which the algorithm adapts in non-ideal classifications cases. One of the most common unsupervised learning algorithms was involved in the proposed algorithm becoming one entity, leading to higher classification accuracy. Additionally, depending on a feature of the K-means algorithm, which one is obtaining different and unfixed results with every implementation? Therefore, the concept of training was simulated in NN, however, the difference is not in updating the weights, as occurs in NN, but in updating the Cores within clusters, which differs in the strength of its representation of clusters.  
The contribution of this paper is as follows:  
1. Introducing a new classification algorithm.
2. Hybridization between classification and clustering to reduce the effect of data distribution problems on the model accuracy.
3. Employing and utilizing the insufficiencies of a specific algorithm in improving the performance of another algorithm by simulating an important concept in building effective models with the highest possible accuracy rate.  
\begin{table}
\begin{tabular}{c c} \hline
**Symbol** & **Explanation** \\ \hline D\_M & Distance matrix \\ \(c1,c2,c3\) & class1, class2, class3 \\ \(d_{51},d_{52},d_{5\times 3}\) & The number of points in each class \\ (D\({}_{\text{M}}\))\({}_{\text{c1}}\), (D\({}_{\text{M}}\))\({}_{\text{c2}}\), (D\({}_{\text{M}}\))\({}_{\text{c3}}\) & Distance matrix for each class \\ (cor\_v)\({}_{\text{c1}}\), (cor\_v)\({}_{\text{c2}}\), (cor\_v)\({}_{\text{c3}}\) & Core vector \\ (cor\_v)\({}_{\text{c1}}\), (cor\_v)\({}_{\text{c3}}\), (cor\_v)\({}_{\text{c1}}\) & The core point which has the highest \\  & connectivity in the class \\ \hline \end{tabular}
\end{table}
Table 1: Notation.