In view of the high computational complexity of the existing centroid solving algorithms leading to the unsatisfactory running speed of interval type-2 fuzzy C-means (IT2FCM) clustering algorithm, two approximate centroid solving algorithms, including half iterative method and one-time iterative method, were proposed. Firstly, based on a direct approach for determining the switch points in the Karnik-Mendel algorithm, with the help of the idea of binary search, a centroid solving algorithm based on binary search was constructed. Based on this algorithm, by limiting the search range, and considering the nature of the relationship between the two conversion points and the skill of calculating the difference, the half iterative method was obtained. Finally, the one-time iterative method was obtained by considering only one search. Five data sets (IRIS, SEEDS, WINE, WIFI_LOCALIZATION and HTRU2) on UCI verified that the clustering performances of the two algorithms were not reduced by solving approximate centroid. Further, eight groups of incremental data were constructed on ANURAN CALLS data set to compare the running speed of IT2FCM based on different centroid solving algorithms and IT2FCM based on the proposed approximate centroid solving algorithm. The experimental results showed that IT2FCM based on approximate centroid solving algorithm ran faster. Therefore, the proposed approximate centroid algorithm can alleviate the high complexity of IT2FCM to a certain extent.