A Survey of Text Clustering Algorithms

By Charu C. Aggarwal, Chengxiang Zhai. Mining Text Data, 2012

759 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/48d277cf8e9b50e53bfcacd535bf834199e976f0

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Xiao Wang
2. Chuan Shi
3. Peng Cui
4. Deyu Bo
5. Jun Zhao
6. Vadlamani Ravi
7. B. Shravan Kumar
8. Zahir Tari

In Information Systems

1. Xiao Wang
2. Jun Zhao
3. Peng Cui
4. Chuan Shi
5. Vadlamani Ravi
6. B. Shravan Kumar
7. Deyu Bo
8. Albert Y. Zomaya

Citations in the corpus

• A survey of the applications of text mining in financial domain (2016, background): “It has various applications like customer segmentation, outlier detection, recommender system, etc. Aggarwal and Zhai (2012) presented a survey paper on text clustering algorithms.”
• A Survey of Clustering Algorithms for Big Data: Taxonomy and Empirical Analysis (2014, background): “Despite a vast number of surveys for clustering algorithms available in the literature [1], [2], [7], and [38] for various domains (such as machine learning, data mining, information retrieval, pattern recognition, bio-informatics and semantic ontology), it is difficult for users to decide a priori ”
• Self-Taught Convolutional Neural Networks for Short Text Clustering (2017, background): “However, short text clustering has the data sparsity problem and most words only occur once in each short text [3].”
• Structural Deep Clustering Network (2020, background): “Over the past decades, a large family of clustering algorithms has been developed and successfully applied to various real-world applications, such as image clustering [28] and text clustering [1].”
• The Medium Is Not the Message: Deconfounding Document Embeddings via Linear Concept Erasure (2025, unknown): “This approach underpins state-of-the-art performance in retrieval (Asai et al., 2021; Thakur et al., 2021; Zhang et al., 2023), clustering (Aggarwal and Zhai, 2012), and classification (Maas et al., 2011).”
• Length-Induced Embedding Collapse in PLM-based Models (2024, unknown): “Text embeddings—dense vectors that capture the semantic information of texts—are essential for many NLP applications, including text analysis (Aggarwal and Zhai, 2012; Angelov, 2020), question answering (Tan et al., 2023; Xu et al., 2024), web search (Zhao et al., 2024; Yates et al., 2021), and…”