In the new era of internet systems and applications, a concept of detecting
distinguished topics from huge amounts of text has gained a lot of attention.
These methods use representation of text in a numerical format -- called
embeddings -- to imitate human-based semantic similarity between words. In this
study, we perform a fuzzy-based analysis of various vector representations of
words, i.e., word embeddings. Also we introduce new methods of fuzzy clustering
based on hybrid implementation of fuzzy clustering methods with an evolutionary
algorithm named Forest Optimization. We use two popular fuzzy clustering
algorithms on count-based word embeddings, with different methods and
dimensionality. Words about covid from Kaggle dataset gathered and calculated
into vectors and clustered. The results indicate that fuzzy clustering
algorithms are very sensitive to high-dimensional data, and parameter tuning
can dramatically change their performance. We evaluate results of experiments
with various clustering validity indexes to compare different algorithm
variation with different embeddings accuracy.

本研究使用模糊聚类方法和进化算法优化实现的模糊聚类方法介绍，通过应用基于计数的单词嵌入在 covid 数据集上，研究表明模糊聚类算法对高维数据非常敏感且参数调整会大大改变其性能，我们通过比较不同算法变体的不同嵌入精度来评估实验结果。