the density of an instance is first calculated, and then compared with that of its neighboring instances. This comparison yields an outlier score that indicates the degree to which the instance deviates from the norm in terms of its density. In this method, normal instances and their neighbors have similar densities, whereas outliers have densities that differ significantly from those of their neighbors. By evaluating the density of an instance related to that of its neighbors, this approach provides an effective means of detecting outliers in a large dataset. The mass-ratio of other instances is defined as the ratio of the number of instances within the sphere of the distance from this computed instance to that of other instances. To understand MOF, consider the original dataset in Fig. 1. (a) and the colored dataset using MOFs in Fig. 1. (b), where the shaded color corresponds to values of MOFs. Instances P2 and P6 in Fig. 1. (b) have the lightest shade since they are placed inside the cluster. Instances P4 and P9 have the darker shade since they are both at the border of the cluster. Instance PS has the darkest shade since it is the farthest away from any cluster. Fig. 3. Plot of synthesized data for dataset no. 2. Fig. 4. Plot of synthesized data for dataset no. 3. TABLE III: DESCRIPTION OF THE DATA SETS USED Table II shows the accuracy of each dataset, and Fig. 7 shows the bar graph representing performance of k-NN, MKNN, and Conglomerate NN. The proposed algorithm exhibits better performance on datasets no.1 and no.2 than k-NN and dataset no.4 shows that it has a better accuracy than MKNN. TABLE IV: THE RESULTS OF THE PROPOSED CLASSIFIER COMPARED TO OTHER CLASSIFIERS-ACCURACY IS THE AVERAGE OF 50 RUNS
