This paper offers a comprehensive overview of machine learning (ML) methodologies and
algorithms, highlighting their practical applications in the critical domain of water resource …

The number of online documents has rapidly grown, and with the expansion of the Web,
document analysis, or text analysis, has become an essential task for preparing, storing …

Unsupervised clustering aims at discovering the semantic categories of data according to
some distance measured in the representation space. However, different categories often …

Autoencoders have become a hot researched topic in unsupervised learning due to their
ability to learn data features and act as a dimensionality reduction method. With rapid …

Background Internet provides different tools for communicating with patients, such as social
media (eg, Twitter) and email platforms. These platforms provided new data sources to shed …

We introduce ClusterLLM, a novel text clustering framework that leverages feedback from an
instruction-tuned large language model, such as ChatGPT. Compared with traditional …

Abstract In Conversational Intent Discovery (CID), Small Language Models (SLMs) struggle
with overfitting to familiar intents and fail to label newly discovered ones. This issue stems …

Text clustering aims to organize a vast collection of documents into meaningful and coherent
clusters, thereby facilitating the extraction of valuable insights. While current frameworks for …

Short text clustering is a challenging task due to the lack of signal contained in short texts. In
this work, we propose iterative classification as a method to boost the clustering quality of …

Clustering short text streams is a challenging task due to its unique properties: infinite
length, sparse data representation and cluster evolution. Existing approaches often exploit …