Reduced Clustering Method Based on the Inversion Formula Density Estimation

Unsupervised learning is one type of machine with an exceptionally high number applications in various fields. The most popular and best-known group unsupervised methods clustering methods. main goal to find hidden relationships between individual observations. There great interest different density estimation methods, especially when there are outliers the data. Density also can be applied data This paper presents extension method based on modified inversion formula solve previous limitations. new method’s works within higher dimensions (d > 15) cases, which was limitation method. More than 20 sets used comparative analysis prove effectiveness developed improvement. results showed that positively affects results. reduced method, estimation, outperforms test sets. In cases accuracy not best, close best models’ obtained accuracies. Lower dimensionality were compare standard extended modification has better all confirmed hypothesis about positive impact