An Immune Network for Contextual Text Data Clustering

2006 ◽  
pp. 432-445 ◽  
Author(s):  
Krzysztof Ciesielski ◽  
Sławomir T. Wierzchoń ◽  
Mieczysław A. Kłopotek
Keyword(s):  
Data Clustering ◽  
Immune Network ◽  
Text Data

Data Clustering with a Neuro-immune Network

2005 ◽  
pp. 1279-1288 ◽  
Author(s):  
Helder Knidel ◽  
Leandro Nunes de Castro ◽  
Fernando J. Von Zuben
Keyword(s):  
Data Clustering ◽  
Immune Network

Assessment of Twitter Data Clusters with Cosine-Based Validation Metrics Using Hybrid Topic Models

2020 ◽  
Vol 25 (6) ◽  
pp. 755-769
Author(s):  
Noorullah R. Mohammed ◽  
Moulana Mohammed
Keyword(s):  
Data Clustering ◽  
Topic Models ◽  
Cluster Validity ◽  
Text Documents ◽  
Text Data ◽  
Validity Assessment ◽  
Text Document ◽  
Cluster Validity Indices ◽  
Validity Indices ◽  
Data Clusters

Text data clustering is performed for organizing the set of text documents into the desired number of coherent and meaningful sub-clusters. Modeling the text documents in terms of topics derivations is a vital task in text data clustering. Each tweet is considered as a text document, and various topic models perform modeling of tweets. In existing topic models, the clustering tendency of tweets is assessed initially based on Euclidean dissimilarity features. Cosine metric is more suitable for more informative assessment, especially of text clustering. Thus, this paper develops a novel cosine based external and interval validity assessment of cluster tendency for improving the computational efficiency of tweets data clustering. In the experimental, tweets data clustering results are evaluated using cluster validity indices measures. Experimentally proved that cosine based internal and external validity metrics outperforms the other using benchmarked and Twitter-based datasets.

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