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The dataset which they have used contain both single-criteria and multiple-criteria user provided ratings. Table 3.4 represents the difference between their way with the conventional approach for both non-clustering and clustering environment. Here, they have compared their experimental result with Pearson collaborative recommender (PCRS) approach. Table 3.4 conveys the result between PCRS with their proposed Mahalanobis distance recommendation scheme (MDRS). It is very transparent that their method MDRS performed much superior than traditional PCRS. These results are based on mean absolute error method. This proposed MDRS approach works better for both on clustering and clustering environments which is shown in Figure 3.6 [2].

They also have shown the graphical implementation of this table which is in both non-clustering and clustering environments. The Mahalanobis distance–based method gives better result than the Pearson collaborative recommender approach. If the mean absolute error values are lower, then it means it is a better result. By that result, we can also see that the non-clustering–based technique always have less performance than clustering approaches. So, the clustering approach is better [2].

Table 3.4 Comparison among clustering and non-clustering approach.

Approach	MAE (non-clustering)	MAE (clustering)
PCRS	2.4577	2.2734
MDRS	2.3094	2.1751

Figure 3.6 Result.

They have consolidated the multiple-criteria ratings into the conventional CF-based recommender system using K-means algorithm. Their method treats the third dimensional as multi-criteria, the clustering parameters as the clustering parameter of the clients, to handle the dimensionality. Their approach depends on thinking like each user has unique opinion and criteria. Therefore, to compare each user, the most important concern of this work is to find out clients’ segments with alike client. Mahalanobis distance method is used here to create most exact neighbors for every client within the cluster. In the result, we can clearly see that this technique is more effective and accurate than the traditional approach [2].