k-Anonymity is a method for providing privacy protection by ensuring that data cannot be traced to an individual. In a k-anonymous dataset, any identifying information occurs in at least k tuples. To achieve optimal and practical k-anonymity, recently, many different kinds of algorithms with various assumptions and restrictions have been proposed with different metrics to measure quality. This paper presents the family of clustering based algorithms that are more flexible and even attempts to improve precision by ignoring the restrictions of user defined Domain Generalization Hierarchies. The main finding of the paper will be that metrics may behave differently through different algorithms and may not show correlations with some applications’ accuracy on output data.