3.1.1. Algorithm

When the numbers of tuples in the processing window reach μ, one round of the clustering algorithm is started to slide again in order to accumulate more tuples in each round [10].

Parameters used in the algorithm are k, u, d:

k defines the parameter for cluster anonymization.

d defines the number of clusters which can be used later.

u defines the processing window size.

### 3.1.2. Drawback

The main drawback of FANNST is that some tuples may remain in the system for more than allowable time constraint. In addition, the time and space complexity of the algorithm is O(S\*S) and not efficient for a data streaming algorithm. Another weakness of FANNST is that it does not support categorical data.

### 3.2. FADS algorithm

The algorithm considers a set as a buffer and saves at most δ tuples in it [11, 12]. Also, another set (setkc) is considered to hold the newly created cluster for later reuse. Each k-anonymized cluster will be remained in setkc up to the reuse constraint Tkc, and after that, the cluster is removed.

### 3.2.1. Drawbacks

The main drawback of the FADS is that the algorithm does not check the remaining time of tuples that hold in the buffer in each round and give their result when they might be considered to have expired. The other important weakness of FADS is that it is not parallel and cannot handle a large number of data streams in tolerable time.
