In the classification of medical data, based on cardiac conditions such as heart rate variability (HRV), clustering is often used to see if HRV records have any rules in the crowd. They were used to determine the feature space and the rates collected at a particular location. Clustering is the process of dividing dataset D into groups of clusters so that objects in the same cluster are more similar to each other than objects in different clusters. Clustering ensembles (CE) are motivated by the fact that the most basic clustering algorithms' performance is highly data-dependent. Specific clustering algorithms can create partitions suitable for given data, proving weak due to some other data. In general, there are two main challenges specific to algorithm clustering. First, various algorithms can find different structures from the same dataset. For example, the K-means may be the best algorithm for spherical clusters. Second, a single clustering algorithm with other parameters can reveal different structures within the same data set. Therefore, choosing the best clustering algorithm for a given dataset is very difficult. Another mechanism to solve this difficult problem is a new method based on some basic partition combinations. The above process is widely referred to as the "cluster ensemble". It aim for a clustering ensemble to combine multiple cluster analysis models for better results than each clustering algorithm in terms of consistency and quality. Clustering an ensemble is usually a two-step algorithm. The first stage stores the results of some independent operations of the K-means method or other clustering algorithms. The second stage uses a specific consensus function to find the last partition in the stored results. Cluster ensemble issues can usually be defined as multiple clusters of a particular dataset, and it turns out that combined clusters have better yield performance. The problem of combining clustering contains some features of the classic clustering problem. The three main issues of the struggle are the functioning of consensus, the diversity of clusters, and the strength of the chemical composition clustering model.