Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
Proximity Estimation and Hardness of Short-Text Corpora
DEXA '08 Proceedings of the 2008 19th International Conference on Database and Expert Systems Application
Clustering Narrow-Domain Short Texts by Using the Kullback-Leibler Distance
CICLing '07 Proceedings of the 8th International Conference on Computational Linguistics and Intelligent Text Processing
Particle Swarm Optimization for clustering short-text corpora
Proceedings of the 2009 conference on Computational Intelligence and Bioengineering: Essays in Memory of Antonina Starita
On the relative hardness of clustering corpora
TSD'07 Proceedings of the 10th international conference on Text, speech and dialogue
Evaluation of internal validity measures in short-text corpora
CICLing'08 Proceedings of the 9th international conference on Computational linguistics and intelligent text processing
An approach to clustering abstracts
NLDB'05 Proceedings of the 10th international conference on Natural Language Processing and Information Systems
ITSA*: an effective iterative method for short-text clustering tasks
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part I
Distributional term representations for short-text categorization
CICLing'13 Proceedings of the 14th international conference on Computational Linguistics and Intelligent Text Processing - Volume 2
An efficient Particle Swarm Optimization approach to cluster short texts
Information Sciences: an International Journal
| Hi-index | 0.00 |
“Short-text clustering” is a very important research field due to the current tendency for people to use very short documents, e.g. blogs, text-messaging and others. In some recent works, new clustering algorithms have been proposed to deal with this difficult problem and novel bio-inspired methods have reported the best results in this area. In this work, a general bio-inspired method based on the AntTree approach is proposed for this task. It takes as input the results obtained by arbitrary clustering algorithms and refines them in different stages. The proposal shows an interesting improvement in the results obtained with different algorithms on several short-text collections.