# Nature-Inspired Optimization Algorithms for Text Document Clustering--A Comprehensive Analysis
## 2 Related Works
### Harmony Search (Hs)  
Clustering analysis is affected by the increasing quantity of text information on Internet web pages. Text clustering is a constructive method of analysis used to divide a large amount of data into clusters. Therefore, the key issue affecting the methodology of text clustering is the inclusion of uninformative and sparse features in text documents. The feature selection (FS) is an important unsupervised method employed to remove uninformative features in effort to enhance text clustering method. Meta-heuristic techniques have recently been successfully implemented to analyze many optimization issues. In Reference [59], Harmony Search (HS) method to overcome the issue of feature selection (FSHSTC) have been proposed. The suggested approach is used to improve the text clustering (TC) technique by acquiring a new subset of informative or useful features. Experiments have been conducted using four datasets. The findings demonstrated that the designed FSHSTC increases the efficiency of the F-measure and Accuracy calculated by k-mean clustering algorithm. The Harmony Search Algorithm (HSA) is an algorithm for optimizing swarm intelligence that has been applied to a wide variety of clustering systems, including data clustering, text clustering, fuzzy clustering, image processing, and wireless sensor networks. In Reference [42], a study of the literature on HSA and its variants, examine its strengths and limitations, and propose potential directions for research have been proposed. In current application areas, such as the World Wide Web, clustering has become an extremely important and highly complex research field for tracking relevant and useful information. Previous works shown that the K-means method, the most widely used partitioning-based clustering technique, is more efficient for big datasets. However, a local optimal clustering can be generated by the K-means method. In Reference [78], a new document clustering algorithms based on the Harmony Search (HS) optimization method have been proposed. first, it proposed a pure HS based clustering algorithm by modeling clustering as an optimization problem, which identifies near-optimal clusters within a reasonable time. Second, harmony clustering is combined in three ways with the K-means method to reach better clustering by integrating HS's exploratory power with the K-means' refining power. In comparison to the K-means method's localized search property, the introduced algorithms conducted a globalized search throughout the solution space. In addition, by having it less dependent on input values such as randomly selected initial cluster centers, the enhanced algorithms strengthen K-means, thereby making it more robust. By modelling its population variability as a Markov chain, the behavior of the introduced method is analyzed. authors also performed experimental analysis to determine the effects of different parameters on the performance of the algorithms' clusters and convergence behavior. In the tests, on five different datasets, it applied the introduced methods along with K-means and a Genetic Algorithm (GA) based clustering algorithm. Experimental findings showed that better clusters can be identified by the proposed algorithms and the output of clusters is reasonable based on F-measure, Entropy, Purity, and Average Distance of Documents to the Centroid cluster (ADDC). text clustering is used in several fields such as text mining, data processing, pattern recognition, image clustering. This chapter [79] demonstrated the methodology projected hybrid function section technique assisted by the harmony search principle for the method of text agglomeration. Irregular harmony memory is generated by the harmony search principle, which includes a list of candidate solutions.