# A Comprehensive Survey of the Harmony Search Algorithm in Clustering Applications
## 4 Clustering Applications of the HSA
### Data Clustering Applications  
K-means clustering is a commonly utilized technique for data and text clustering, used in the data-mining domain due to its homogeneity and high velocity in grouping massive datasets [97; 98; 99]. However, K-means has two main weaknesses, one involving the initial centroid and the other involving convergence to the exploration area.  
[PERSON] et al. in [76] introduced a new method using HSA to overcome the weaknesses of the K-means technique. The HSA does not need an initial centroid and utilizes a stochastic search rather than a gradient search, so secondary data is unnecessary. The proposed HSA was compared with other heuristic algorithms by executing them all on different simulations and real datasets. The results obtained showed that the proposed HSA is a robust clustering technique, and obtains more precise clustering results than similar algorithms.  
[PERSON] et al. in [75] presented a new clustering technique based on using the HSA, called IHSK, with natural selection utility. The proposed algorithm is combined with the basic HSA and K-means clustering. The selection method is used to train the low probability neighborhood of positions and move them toward the optimal solution. The proposed IHSK algorithm was tested on collections of synthetic and real datasets. The results were promising in comparison to those from similar clustering techniques published in the literature.  
Clustering problems have become a common and vital task in modern computer science applications. [PERSON] et al. in [72] introduced new clustering methods using HSA for web document clustering purposes. The clustering task is presented as an optimization problem, and basic HSA-based clustering is employed initially to find near-optimal clusters within a reduced time. Then, a hybrid method using K-means and HSA is proposed to perform more reliable clustering. It was reported that the hybrid HSA can obtain better clusters using several text datasets when compared to related methods. Moreover, it converges more rapidly to the best-recognized optimum.  
[PERSON] et al. in [86] introduced a novel HSA for addressing optimization problems, called VHS. VHS combines principles taken from the optimization system to enhance the performance of the HSA. This reduces the problem of fixed-parameter context inside the HS rule-set. VHS utilizes a variety of candidate solutions to improve solution positions. The effects of scalability, turbulence, HM length, and HMCR have additionally been studied with the proposed HSA. VHS is then implemented for the documents clustering problem. Four real-life datasets were selected from the UCI. The proposed VHS clustering approach produced good results compared to the different clustering algorithms.  
As evident in the literature of the HSA for solving the data clustering problems, most of the researches have been conducted using several versions of the HSA to solve clustering problems, especially the data clustering problems, which means that data clustering applications still have a wide space for further investigations and analysis.