A Novel Weighting Scheme Applied to Improve the Text Document Clustering Techniques

Abualigah, Laith; Khader, Ahamad Tajudin; Hanandeh, Essam Said

doi:10.1007/978-3-319-66984-7_18

Cited by 33 publications

(10 citation statements)

References 28 publications

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“…Reference [82] proposed a new method to solve the text clustering issue called length feature weight (LFW). This approach enhances the text clustering by providing a fair weighting value of the most important features for each document.…”

Section: K-means Clustering Techniquementioning

confidence: 99%

Nature-Inspired Optimization Algorithms for Text Document Clustering—A Comprehensive Analysis

et al. 2020

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Text clustering is one of the efficient unsupervised learning techniques used to partition a huge number of text documents into a subset of clusters. In which, each cluster contains similar documents and the clusters contain dissimilar text documents. Nature-inspired optimization algorithms have been successfully used to solve various optimization problems, including text document clustering problems. In this paper, a comprehensive review is presented to show the most related nature-inspired algorithms that have been used in solving the text clustering problem. Moreover, comprehensive experiments are conducted and analyzed to show the performance of the common well-know nature-inspired optimization algorithms in solving the text document clustering problems including Harmony Search (HS) Algorithm, Genetic Algorithm (GA), Particle Swarm Optimization (PSO) Algorithm, Ant Colony Optimization (ACO), Krill Herd Algorithm (KHA), Cuckoo Search (CS) Algorithm, Gray Wolf Optimizer (GWO), and Bat-inspired Algorithm (BA). Seven text benchmark datasets are used to validate the performance of the tested algorithms. The results showed that the performance of the well-known nurture-inspired optimization algorithms almost the same with slight differences. For improvement purposes, new modified versions of the tested algorithms can be proposed and tested to tackle the text clustering problems.

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Section: K-means Clustering Techniquementioning

confidence: 99%

Nature-Inspired Optimization Algorithms for Text Document Clustering—A Comprehensive Analysis

et al. 2020

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“…Precision is a positive predictive value which is a part of the cases retrieved relevant from all cases retrieved from the results of the process [26,27,28]. Where precision is represented by the following equation:…”

Section: Precisionmentioning

confidence: 99%

Improved ICHI square feature selection method for Arabic classifiers

Alshaer

Otair

Abualigah

2020

IJ-ICT

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<span>Feature selection problem is one of the main important problems in the text and data mining domain. </span><span>This paper presents a comparative study of feature selection methods for Arabic text classification. Five of the feature selection methods were selected: ICHI square, CHI square, Information Gain, Mutual Information and Wrapper. It was tested with five classification algorithms: Bayes Net, Naive Bayes, Random Forest, Decision Tree and Artificial Neural Networks. In addition, Data Collection was used in Arabic consisting of 9055 documents, which were compared by four criteria: Precision, Recall, F-measure and Time to build model. The results showed that the improved ICHI feature selection got almost all the best results in comparison with other methods.</span>

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“…Also, a heuristic is suitable to treat other parts of huge data, such as diversity, and speed [6]. Heuristics-based exploration plan displays two criteria are exploitation to govern neighbor and the search area [7], [52], [53].…”

Section: Related Workmentioning

confidence: 99%

Optimization Algorithms to Solve Feature Selection Problem: A Review

Abualigah¹

2019

IJSAIT

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Feature Selection (FS) method is one of the most important data pre-processing steps in data mining domain, it is used to find the essential features subset in order to make a new subset of informative features. The model that used the informative subset such that a classification model built only with this subset would get better predictive accuracy than the model that used a complete set of features. In this paper, we provide a summary of almost all of the methods present in the literature of 2018. The goal is to provide a general presentation to variant optimization algorithms that can be applied to a wide array of machine learning problems. We converge on Filter, Wrapper and Embedded methods. Finally, we analyzed the results of several feature selection techniques on standard datasets to illustrate the applicability of feature selection techniques.

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A Novel Weighting Scheme Applied to Improve the Text Document Clustering Techniques

Cited by 33 publications

References 28 publications

Nature-Inspired Optimization Algorithms for Text Document Clustering—A Comprehensive Analysis

Nature-Inspired Optimization Algorithms for Text Document Clustering—A Comprehensive Analysis

Improved ICHI square feature selection method for Arabic classifiers

Optimization Algorithms to Solve Feature Selection Problem: A Review

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