What is a disadvantage of using Cubic interpolation?

Cubic interpolation is a method used to estimate values at unknown points based on the surrounding known data points. One of the key disadvantages of this method is its computational complexity, which makes it time-consuming due to the processing requirements. Cubic interpolation involves calculations that take into account a greater number of surrounding values to achieve higher accuracy in the smooth curve it establishes between known data points. This increased processing time can become an issue, particularly with large datasets or when real-time processing is necessary.

The other options present characteristics that do not accurately reflect the nature of cubic interpolation. For instance, it indeed requires surrounding cell values for accurate interpolation, hence it's not inaccurate to state that it does not require them. Regarding the use of fewer surrounding cells, cubic interpolation actually requires multiple data points, which is contrary to this implication. Lastly, while cubic interpolation can handle varying distributions of data, it can struggle with very irregular or clustered distributions, but its inability to handle clustered data isn't a definitive characteristic limiting its application like the processing time requirement is.