Text document clustering is to divide textual contents into clusters or groups. It received wide
attention due to the vast amount of daily data from the Web. In the last decade, Meta …

The clustering of big data is a challenging task. The traditional clustering algorithms are
inefficient for clustering big data. The recent researches in this field suggest that the …

Text classification (TC) is a crucial practice in case of organizing a vast number of
documents. The computational complexity of the TC process is usually high because of the …

Efficient topic modeling is needed to support applications that aim at identifying main themes
from a collection of documents. In the present paper, a reduced vector embedding …

In this paper, we propose a novel clustering algorithm which uses a weighted combination of
several criteria as its fitness function. We demonstrate the suitability of the new method in the …

Text document clustering is one of the data mining techniques used in many real-world
applications such as information retrieval from IoT Sensors data, duplicate content detection …

In this paper, we propose a new approach to solve the document-clustering using the K-
Means algorithm. The latter is sensitive to the random selection of the k cluster centroids in …

Clustering a set of data into homogeneous groups is a fundamental operation in data
mining. Recently, consideration has been put on categorical data clustering, where the data …

Text clustering is an unsupervised learning technique that divides a large number of text
documents into a small number of clusters. Each cluster contains similar documents, while …