… DDNN alleviated noise and raised the performance of feature extraction. Likewise, in
order to resolve the problem of computing high dimensional sparse matrix for the task of text …

… is an unsupervised technique, which organizes the data into groups based on the … cluster
must have a high degree of similarity. The data objects in different clusters must have a high …

… Text document clustering has become an important and fast-growing research area, due
to the massive amounts of text data produced by the Internet, social media, email and text …

… clustering of the extracted features in the aim to find the visual words and histogram
representation. Various techniques were exploited to cluster the large number of features, … of large …

… modeling, classification, and clustering. Hence… large Wikipedia corpus on Nepali language
and a recent study on NepCov19Tweets dataset shows that FastText-based feature extraction …

… that documents within a specific cluster possess high intra-… documents are not given
beforehand (eg, politics), the focus of this paper on partition clustering procedures as this clustering …

… Hence, the use of pre-trained models for feature extraction is also a major … the huge amount
of user-generated texts, extraction of significant information from numerous documents has …

… Moreover, the proposed system adopts a hybrid approach to the classification process by …
word/term has high frequency in a document and the number of documents in which the word/…

… features then the feature vector of larger documents will grow big enough that it would
not fit in the memory. Hence, in order to solve this problem, we need to optimize the feature …

… used to partition a huge number of text documents into a subset of clusters. In which, each
… for large and high-dimensional datasets such as document clustering, the proposed hybrid …