Comparative Advantage Approach for Sparse Text Data Clustering

2009 ◽  
Author(s):  
Jie Ji ◽  
Tony Y.T. Chan ◽  
Qiangfu Zhao
Keyword(s):  
Comparative Advantage ◽  
Data Clustering ◽  
Text Data

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.

Text Data Clustering by Contextual Graphs

2006 ◽  
pp. 65-76 ◽  
Author(s):  
Krzysztof Ciesielski ◽  
Mieczysław A. Kłopotek
Keyword(s):  
Data Clustering ◽  
Text Data ◽  
Contextual Graphs

scholarly journals High-Dimensional Text Clustering by Dimensionality Reduction and Improved Density Peak

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Yujia Sun ◽  
Jan Platoš
Keyword(s):  
Dimensionality Reduction ◽  
Data Clustering ◽  
High Dimensional Data ◽  
Random Projection ◽  
Experimental Results ◽  
High Dimensional ◽  
Density Peak ◽  
Text Data ◽  
Number Of Clusters ◽  
Density Peaks

This study focuses on high-dimensional text data clustering, given the inability of K-means to process high-dimensional data and the need to specify the number of clusters and randomly select the initial centers. We propose a Stacked-Random Projection dimensionality reduction framework and an enhanced K-means algorithm DPC-K-means based on the improved density peaks algorithm. The improved density peaks algorithm determines the number of clusters and the initial clustering centers of K-means. Our proposed algorithm is validated using seven text datasets. Experimental results show that this algorithm is suitable for clustering of text data by correcting the defects of K-means.

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

scholarly journals Efficient Big Text Data Clustering Algorithms using Hadoop and Spark

2021 ◽  
Vol 174 (15) ◽  
pp. 13-21
Author(s):  
Sergios Gerakidis ◽  
Sofia Megarchioti ◽  
Basilis Mamalis
Keyword(s):  
Data Clustering ◽  
Clustering Algorithms ◽  
Text Data
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