A General Bio-inspired Method to Improve the Short-Text Clustering Task

Ingaramo, Diego Alejandro; Errecalde, Marcelo Luis; Rosso, Paolo

doi:10.1007/978-3-642-12116-6_56

Cited by 7 publications

(16 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results of CLUDIPSO ⋆ were compared with those obtained by other three algorithms: K-Means [33], K-MajorClust 13 [25] and CHAMELEON [29]. K-Means is one of the most popular clustering algorithms whereas K-MajorClust and CHAMELEON are representative of the density and graph-based approaches to the clustering problem and have shown interesting results in similar problems.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

An efficient Particle Swarm Optimization approach to cluster short texts

Cagnina

Errecalde

Ingaramo

et al. 2014

Information Sciences

Self Cite

View full text Add to dashboard Cite

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 this context, the clustering of short texts is a significant analysis task and a discrete Particle Swarm Optimization (PSO) algorithm named CLUDIPSO has recently shown a promising performance in this type of problems. CLUDIPSO obtained high quality results with small corpora although, with larger corpora, a significant deterioration of performance was observed. This article presents CLUDIPSO ⋆ , an improved version of CLUDIPSO, which includes a different representation of particles, a more efficient evaluation of the function to be optimized and some modifications in the mutation operator. Experimental results with corpora containing scientific abstracts, news and short legal documents obtained from the Web, show that CLUDIPSO ⋆ is an effective clustering method for short-text corpora of small and medium size.

show abstract

Section: Resultsmentioning

confidence: 99%

“…The results are compared with those obtained by other three representative clustering algorithms: K-Means [33], K-MajorClust [25] and CHAMELEON [29].…”

Section: Introductionmentioning

confidence: 99%

An efficient Particle Swarm Optimization approach to cluster short texts

Cagnina

Errecalde

Ingaramo

et al. 2014

Information Sciences

Self Cite

View full text Add to dashboard Cite

show abstract

“…Furthermore, the silhouette coefficient has also shown its potential for determining the optimal number of groups in a clustering problem (Rousseeuw 1987, Tan et al 2005, Choi, Tan, Anandkumar and Willsky 2011, estimating how difficult a corpus is for an arbitrary clustering algorithm , computing a target function to be optimized (Cagnina et al 2008, Ingaramo et al 2009), automatically determining a threshold for a similarity function (Bonato dos Santos, Heuser, Pereira Moreira and Krug Wives 2011) and as a key component in other internal process of clustering algorithms (Ingaramo et al 2010a, Aranganayagi and Thangavel 2007.…”

Section: The Silhouette Coefficientmentioning

confidence: 99%

“…This idea was later generalized and served as antecedent for the approach of using Sil-Att as a general improvement method. In (Ingaramo et al 2010a), a simplified and more general version of AntSA-CLU is presented, named Partitional AntSA ⋆ (PAntSA ⋆ ). PAntSA ⋆ is the partitional version of the hierarchical AntSA-CLU algorithm where, furthermore, it is not assumed as input the results of any particular clustering algorithm.…”

Section: Related Workmentioning

confidence: 99%

“…In particular, some recent bio-inspired proposals have gained increasing interest in short-text clustering. These approaches include algorithms based on Particle Swarm Optimization (PSO) techniques Rosso 2008, Ingaramo, Errecalde, Cagnina andRosso 2009) and ant-behavior-based approaches (Ingaramo, Errecalde and Rosso 2010b, Ingaramo, Errecalde and Rosso 2010a.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Silhouette + attraction: A simple and effective method for text clustering

Errecalde¹,

Cagnina²,

Rosso³

2015

Nat. Lang. Eng.

Self Cite

View full text Add to dashboard Cite

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 us to obtain a general technique that can be used either as a boosting method, which improves results of other clustering algorithms, or as an independent clustering algorithm. The experimental work shows that Sil-Att is able to obtain high quality results on text corpora with very different characteristics. Furthermore, its stable performance on all the considered corpora is indicative that it is a very robust method. This is a very interesting positive aspect of Sil-Att with respect to the other algorithms used in the experiments, whose performances heavily depend on specific characteristics of the corpora being considered.

show abstract

ITSA ⋆ : An Effective Iterative Method for Short-Text Clustering Tasks

Errecalde¹,

Ingaramo²,

Rosso³

2010

Trends in Applied Intelligent Systems

Self Cite

View full text Add to dashboard Cite

Abstract. The current tendency for people to use very short documents, e.g. blogs, text-messaging, news and others, has produced an increasing interest in automatic processing techniques which are able to deal with documents with these characteristics. In this context, "short-text clustering" is a very important research field where new clustering algorithms have been recently proposed to deal with this difficult problem. In this work, ITSA , an iterative method based on the bio-inspired method PAntSA is proposed for this task. ITSA takes as input the results obtained by arbitrary clustering algorithms and refines them by iteratively using the PAntSA algorithm. The proposal shows an interesting improvement in the results obtained with different algorithms on several short-text collections. However, ITSA can not only be used as an effective improvement method. Using random initial clusterings, ITSA outperforms well-known clustering algorithms in most of the experimental instances.

show abstract

A General Bio-inspired Method to Improve the Short-Text Clustering Task

Cited by 7 publications

References 5 publications

An efficient Particle Swarm Optimization approach to cluster short texts

An efficient Particle Swarm Optimization approach to cluster short texts

Silhouette + attraction: A simple and effective method for text clustering

ITSA ⋆ : An Effective Iterative Method for Short-Text Clustering Tasks

Contact Info

Product

Resources

About