Word Embeddings and Validity Indexes in Fuzzy Clustering
This work addresses text analysis for researchers by introducing hybrid fuzzy clustering methods, but it is incremental as it builds on existing techniques.
The study tackled the problem of detecting topics from text by analyzing fuzzy clustering on word embeddings, finding that fuzzy clustering algorithms are highly sensitive to high-dimensional data and parameter tuning significantly affects performance.
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.