A Novel Approach in Clustering Algorithm to Evaluate the Performance of Regression Analysis

This paper, introduced a new methodology to raise the metric of a journal's impact. This method is depending on finding clusters from SC Imago database and creates datasets utilizing a modified k-means clustering algorithm. Farther, developing of linear regression analysis on these datasets is perplexed by seeing index values are dependent variables and citation parameters as independent variables result in assessing contributing factors to increase bibliometric index of any journal. next step, cluster quality metrics enforced to evaluate the perfectness of fit of the cluster such as homogeneity score, completeness score, V measure, accommodated rand score and silhouette coefficient. The output of modified k-means algorithm on a dataset of 1445 journals resulted in 3 clusters (k=3). Each cluster data clustered depending on the title. The regression analysis states that the publisher who desires to enhance his journal bibliometric indexes should deliberate the advice conferred, in this work, bring large number of paper submissions to their journal especially. Almost four indices which are of main importance in the publisher industry having been used this. The analysis ensure in strong advantage as the testing of output produced including regression parameters clarified with the identification of outliers by the inclusion of relative error calculation. Accordingly, seeing the suggestive features with increase or decrease in TD3, TC3, CD3, CD2 and RD values, the publisher would profit from raising their respective bibliometric index.