  Fuzzy clustering is a famous unsupervised learning method used to collecting
similar data elements within cluster according to some similarity measurement.
But, clustering algorithms suffer from some drawbacks. Among the main weakness
including, selecting the initial cluster centres and the appropriate clusters
number is normally unknown. These weaknesses are considered the most
challenging tasks in clustering algorithms. This paper introduces a
comprehensive review of metahueristic search to solve fuzzy clustering
algorithms problems.
