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Topic Models and Fusion Methods: a Union to Improve Text Clustering and Cluster Labeling
Topic modeling algorithms are statistical methods that aim to discover the topics running through the text documents. Using topic models in machine learning and text mining is popular due to its applicability in inferring ...
An efficient Particle Swarm Optimization approach to cluster short texts
(Elsevier, 2014-05)
Short texts such as evaluations of commercial products, news, FAQ’s and scientific abstracts are important resources on the Web due to the constant requirements of people to use this on line information in real life. In ...
Named entities as privileged information for hierarchical text clustering
(Association for Computing Machinery - ACMInstituto Superior de Engenharia do Porto - ISEPPorto, 2014-07)
Text clustering is a text mining task which is often used to aid the organization, knowledge extraction, and exploratory search of text collections. Nowadays, the automatic text clustering becomes essential as the volume ...
Improving hierarchical document cluster labels through candidate term selection
(2012-09-03)
One way to organize knowledge and make its search and retrieval easier is to create a structural representation divided by hierarchically related topics. Once this structure is built, it is necessary to find labels for ...
Interactive textual feature selection for consensus clustering
(ElsevierAmsterdam, 2015-01)
Consensus clustering and interactive feature selection are very useful methods to extract and manage knowledge from texts. While consensus clustering allows the aggregation of different clustering solutions into a single ...
Comparative Study of Clustering Algorithms in Text Mining Context
The spectacular increasing of Data is due to
the appearance of networks and smartphones. Amount 42% of
world population using internet [1]; have created a problem
related of the processing of the data exchanged, which ...
An Investigation Into Different Text Representations to Train an Artificial Immune Network for Clustering Texts
Extracting knowledge from text data is a complex task that is usually performed by first structuring the texts and then applying machine learning algorithms, or by using specific deep architectures capable of dealing ...
Silhouette + Attraction: A Simple and Effective Method for Text Clustering
(Cambridge University Press, 2015-08-14)
This article presents Sil-Att, a simple and effective method for text clustering, which is based on two main concepts: the silhouette coefficient and the idea of attraction. The combination of both principles allows to ...
Selecting candidate labels for hierarchical document clusters using association rules
(2010-12-16)
One way to organize knowledge and make its search and retrieval easier is to create a structural representation divided by hierarchically related topics. Once this structure is built, it is necessary to find labels for ...
Improving hierarchical document cluster labels through candidate term selection
(2012-09-03)
One way to organize knowledge and make its search and retrieval easier is to create a structural representation divided by hierarchically related topics. Once this structure is built, it is necessary to find labels for ...