Unsupervised text feature selection technique based on hybrid particle swarm optimization algorithm with genetic operators for the text clustering

Abualigah, Laith; Khader, Ahamad Tajudin

doi:10.1007/s11227-017-2046-2

Cited by 376 publications

(89 citation statements)

References 26 publications

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“…Also, it does not need post-processing after the clustering is done [23]. Aside of ACO, some of the proposed clustering algorithms also use the same concept of solution string as ACO based clustering [12], [24]- [26].…”

Section: Related Workmentioning

confidence: 99%

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Hybrid Nearest Neighbors Ant Colony Optimization for Clustering Social Media Comments

Lucky

Girsang

2020

IJCAI

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Ant colony optimization (ACO) is one of robust algorithms for solving optimization problems, including clustering. However, high and redundant computation is needed to select the proper cluster for each object, especially when the data dimensionality is high, such as social media comments. Reducing the redundant computation may cut the execution time, but it can potentially decrease the quality of clustering. With the basic idea that nearby objects tend to be in the same cluster, the nearest neighbors method can be used to choose the appropriate cluster for some objects efficiently by considering their neighbor's cluster. Therefore, this paper proposes the combination of nearest neighbors and ant colony optimization for clustering (NNACOC) which can reduce the computation time but is still able to retain the quality of clustering. To evaluate its performance, NNACOC was tested using some benchmark datasets and twitter comments. Most of the experiments show that NNACOC outperformed the original ant colony optimization for clustering (ACOC) in quality and execution time. NNACOC also yielded a better result than k-means when clustering the twitter comments. Povzetek: Predstavljen je hibridni algoritem najbližjih sosedov z optimizacijo mravljinčnih kolonij za grupiranje komentarjev socialnih medijev.

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Section: Related Workmentioning

confidence: 99%

“…Then, it is normalized or reduced by its occurrence frequencies across the document collection (IDF). The TF-IDF method is also used in some of text clustering studies for vectorizing the text document [18], [24]- [26].…”

Section: N-nearest Neighbors Constructionmentioning

confidence: 99%

Hybrid Nearest Neighbors Ant Colony Optimization for Clustering Social Media Comments

Lucky

Girsang

2020

IJCAI

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“…Likewise LSI, LDA utilizes word patterns for co-occurrence,,representation of documents. 3-Level Bayesian hierarchical model [27], [9 ], [15]used in LDA that models every individual data item in a given input document collection.. Gidds sampling [28] uses to weight the value of each feature. The document can be represent as [Li1, Li2,… Lij, …Lik]where Lij is the jth value function in the frequency space of the k-dimensional subject.…”

Section: Latent Dirichlet Allocationmentioning

confidence: 99%

A Probe on Document Clustering Methodologies and Its Performance Metrics

Kalpana¹

2019

IJRTE

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“…To attain diminished set of features for the issue of classification, several feature selections models (wrapper & filter) is implemented by various researchers to enhance the accuracy of classifiers. The work [6] proposed a feature selection algorithm known as particle-swarm optimization (PSO) to cluster the text. The work [7] presents that the model based on wrapper to choose optimal genes from the dataset of microarray is projected with an approach of Markova blanket.…”

Section: Related Workmentioning

confidence: 99%

Bipartite Weighted Graph Access for Optimal Label Prediction

2019

IJRTE

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This article portrayed a novel bipartite weighted graph strategy for Feature optimization for machine learning models. Unlike many of the existing optimization techniques of diverse categories such as evolutionary computation techniques, diversity assessment strategies, the proposal is deterministic approach with minimal computational overhead, which has referred further as Bipartite Weighted Graph Approach for Optimal label prediction (BWG-OLP). The proposed model is about to derive a given feature is optimal or not by the respective feature’s correlation with the records and the correlation with the fellow features. The experimental study has carried on benchmark datasets to estimate the significance of the proposed method.

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Unsupervised text feature selection technique based on hybrid particle swarm optimization algorithm with genetic operators for the text clustering

Cited by 376 publications

References 26 publications

Hybrid Nearest Neighbors Ant Colony Optimization for Clustering Social Media Comments

Hybrid Nearest Neighbors Ant Colony Optimization for Clustering Social Media Comments

A Probe on Document Clustering Methodologies and Its Performance Metrics

Bipartite Weighted Graph Access for Optimal Label Prediction

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