Clustering Text Documents with Adaptive Dimensionality Reduction

The article discusses a new method for clustering data called "Adaptive K-Means with Non-negative Matrix Factorization." The authors aim to improve upon traditional K-means clustering by incorporating non-negative matrix factorization, which helps to reduce the effect of outliers and improve the quality of clusters.
The authors begin by explaining that traditional K-means clustering has some limitations, such as being sensitive to outliers and not able to handle high-dimensional data effectively. They then introduce their new method, which combines the strengths of K-means with non-negative matrix factorization.
Non-negative matrix factorization is a technique that separates a matrix into two non-negative matrices, one representing the factors and the other representing the loadings. By using this technique, the authors are able to reduce the dimensionality of the data while preserving the non-negativity of the features.
The authors then describe how their method works in practice. They first convert the original data into a matrix of binary vectors, called the "embedded features," and then apply non-negative matrix factorization to the embedded features. The resulting matrices are used as inputs to the K-means algorithm, which clusters the data into groups based on their similarity.
The authors test their method on several real-world datasets and show that it outperforms traditional K-means clustering in terms of accuracy and efficiency. They also demonstrate the effectiveness of their method on synthetic datasets with different characteristics.
Overall, the article provides a detailed explanation of the new method and its applications, as well as thorough evaluations of its performance on various datasets. The authors also provide insights into the theoretical basis of their method and its advantages over traditional clustering techniques.