Clustering is used to find structure in unlabeled data sets, which is one of the fundamental problems for advanced data mining steps. They play an important role in many fields, especially big data analysis and mining. Usually, clustering is based on the similarity between data samples in a cluster or the difference between data samples in two different clusters. However, there are data samples located at the border of clusters, and it is difficult to determine which cluster they belong to. Clustering algorithms based on fuzzy sets have advantages in handling uncertain data, but they have difficulties with large data, noisy data, outliers, data with unclear cluster boundaries, etc. The paper presents the border fuzzy c-means clustering algorithm to minimize the above disadvantages. The proposed algorithm consists of two stages, initializing the initial cluster center based on the data distribution; stage 2 describes the proposed algorithms border fuzzy c-means clustering (B-FCM) and border semi-supervised fuzzy c-means clustering techniques (B-SSFCM) in which data samples located on the fuzzy boundaries of clusters are considered based on border information to decide which cluster they belong to. Stage 2 is repeated until the samples on the boundaries are assigned to clusters. This approach also helps to quickly assign edge data samples to clusters, thereby helping the algorithm converge faster. Experiments on the proposed method on three large data sets show that the proposed method not only gives better clustering results but also gives much faster time than some other algorithms.