Laith Mohammad Abualigah

A comprehensive review

Graphical abstractDisplay Omitted HighlightsThe comprehensive review of Krill Herd Algorithm as applied to different domain is presented.The review covers the applications, modifications and hybridizations of the KH algorithms.It provides future research directions across different areas. Krill Herd (KH) algorithm is a class of nature-inspired algorithm, which simulates the herding behavior of krill individuals. It has been successfully utilized to tackle many optimization problems in different domains and found to be very efficient. As a result, the studies has expanded significantly in the last 3 years. This paper presents the extensive (not exhaustive) review of KH algorithm in the area of applications, modifications, and hybridizations across these fields. The description of how KH algorithm was used in the approaches for solving these kinds of problems and further research directions are also discussed.

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

The text clustering technique is an appropriate method used to partition a huge amount of text documents into groups. The documents size affects the text clustering by decreasing its performance. Subsequently, text documents contain sparse and uninformative features, which reduce the performance of the underlying text clustering algorithm and increase the computational time. Feature selection is a fundamental unsupervised learning technique used to select a new subset of informative text features to improve the performance of the text clustering and reduce the computational time. This paper proposes a hybrid of particle swarm optimization algorithm with genetic operators for the feature selection problem. The k-means clustering is used to evaluate the effectiveness of the obtained features subsets. The experiments were conducted using eight common text datasets with variant characteristics. The results show that the proposed algorithm hybrid algorithm (H-FSPSOTC) improved the performance of the clustering algorithm by generating a new subset of more informative features. The proposed algorithm is compared with the other comparative algorithms published in the literature. Finally, the feature selection technique encourages the clustering algorithm to obtain accurate clusters.

Text feature selection with a robust weight scheme and dynamic dimension reduction to text document clustering

Three meta-heuristic algorithms are adapted to solve the feature selection problem.Feature selection methods are established based on a novel weighting scheme.Dimension reduction technique is proposed to reduce the number of features.K-mean clustering algorithm is used based on the features obtained.The proposed methods outperform the comparative methods. This paper proposes three feature selection algorithms with feature weight scheme and dynamic dimension reduction for the text document clustering problem. Text document clustering is a new trend in text mining; in this process, text documents are separated into several coherent clusters according to carefully selected informative features by using proper evaluation function, which usually depends on term frequency. Informative features in each document are selected using feature selection methods. Genetic algorithm (GA), harmony search (HS) algorithm, and particle swarm optimization (PSO) algorithm are the most successful feature selection methods established using a novel weighting scheme, namely, length feature weight (LFW), which depends on term frequency and appearance of features in other documents. A new dynamic dimension reduction (DDR) method is also provided to reduce the number of features used in clustering and thus improve the performance of the algorithms. Finally, k-mean, which is a popular clustering method, is used to cluster the set of text documents based on the terms (or features) obtained by dynamic reduction. Seven text mining benchmark text datasets of different sizes and complexities are evaluated. Analysis with k-mean shows that particle swarm optimization with length feature weight and dynamic reduction produces the optimal outcomes for almost all datasets tested. This paper provides new alternatives for text mining community to cluster text documents by using cohesive and informative features.

Unsupervised feature selection technique based on genetic algorithm for improving the Text Clustering

The increasing amount of text documents in digital forms affect the text analysis techniques. Text clustering (TC) is one of the important techniques used for showing a massive amount of text documents by clusters. Hence, the main problem that affects the text clustering technique is the presence sparse and uninformative features on the text documents. The feature selection (FS) is an essential unsupervised learning technique. This technique is used to select informative features to improve the performance of text clustering algorithm. Recently, the meta-heuristic algorithms are successfully applied to solve several hard optimization problems. In this paper, we proposed the genetic algorithm (GA) to solve the unsupervised feature selection problem, namely, (FSGATC). This method is used to create a new subset of informative features in order to obtain more accurate clusters. Experiments were conducted using four benchmark text datasets with variant characteristics. The results showed that the proposed FSGATC is improved the performance of the text clustering algorithm and got better results compared with k-mean clustering standalone. Finally, the proposed method “FSGATC” evaluated by F-measure and Accuracy, which are common measures used in the domain of text clustering.

A novel hybridization strategy for krill herd algorithm applied to clustering techniques

Abstract Krill herd (KH) is a stochastic nature-inspired optimization algorithm that has been successfully used to solve numerous complex optimization problems. This paper proposed a novel hybrid of KH algorithm with harmony search (HS) algorithm, namely, H-KHA, to improve the global (diversification) search ability. The enhancement includes adding global search operator (improvise a new solution) of the HS algorithm to the KH algorithm for improving the exploration search ability by a new probability factor, namely, Distance factor, thereby moving krill individuals toward the best global solution. The effectiveness of the proposed H-KHA is tested on seven standard datasets from the UCI Machine Learning Repository that are commonly used in the domain of data clustering, also six common text datasets that are used in the domain of text document clustering. The experiments reveal that the proposed hybrid KHA with HS algorithm (H-KHA) enhanced the results in terms of accurate clusters and high convergence rate. Mostly, the performance of H-KHA is superior or at least highly competitive with the original KH algorithm, well-known clustering techniques and other comparative optimization algorithms.

A krill herd algorithm for efficient text documents clustering

Recently, due to the huge growth of web pages, social media and modern applications, text clustering technique has emerged as a significant task to deal with a huge amount of text documents. Some web pages are easily browsed and tidily presented via applying the clustering technique in order to partition the documents into a subset of homogeneous clusters. In this paper, two novel text clustering algorithms based on krill herd (KH) algorithm are proposed to improve the web text documents clustering. In the first method, the basic KH algorithm with all its operators is utilized while in the second method, the genetic operators in the basic KH algorithm are neglected. The performance of the proposed KH algorithms is analyzed and compared with the k-mean algorithm. The experiments were conducted using four standard benchmark text datasets. The results showed that the proposed KH algorithms outperformed the k-mean algorithm in term of clusters quality that is evaluated using two common clustering measures, namely, Purity and Entropy.

A new feature selection method to improve the document clustering using particle swarm optimization algorithm

Abstract The large amount of text information on the Internet and in modern applications makes dealing with this volume of information complicated. The text clustering technique is an appropriate tool to deal with an enormous amount of text documents by grouping these documents into coherent groups. The document size decreases the effectiveness of the text clustering technique. Subsequently, text documents contain sparse and uninformative features (i.e., noisy, irrelevant, and unnecessary features), which affect the effectiveness of the text clustering technique. The feature selection technique is a primary unsupervised learning method employed to select the informative text features to create a new subset of a documents features. This method is used to increase the effectiveness of the underlying clustering algorithm. Recently, several complex optimization problems have been successfully solved using metaheuristic algorithms. This paper proposes a novel feature selection method, namely, feature selection method using the particle swarm optimization (PSO) algorithm (FSPSOTC) to solve the feature selection problem by creating a new subset of informative text features. This new subset of features can improve the performance of the text clustering technique and reduce the computational time. Experiments were conducted using six standard text datasets with several characteristics. These datasets are commonly used in the domain of the text clustering. The results revealed that the proposed method (FSPSOTC) enhanced the effectiveness of the text clustering technique by dealing with a new subset of informative features. The proposed method is compared with the other well-known algorithms i.e., feature selection method using a genetic algorithm to improve the text clustering (FSGATC), and feature selection method using the harmony search algorithm to improve the text clustering (FSHSTC) in the text feature selection.

Multi-objectives-based text clustering technique using K-mean algorithm

Text documents clustering is a popular unsupervised text mining tool. It is used for partitioning a collection of text documents into similar clusters based on the distance or similarity measure as decided by an objective function. Text clustering algorithm often makes prior assumptions to satisfy objective function, which is optimized either through traditional techniques or meta-heuristic techniques. In text clustering techniques, the right decision for any document distribution is done using an objective function. Normally, clustering algorithms perform poorly when the configuration of the well-formulated objective function is not sound and complete. Therefore, we proposed multi-objectives-based method namely, combine distance and similarity measure for improving the text clustering technique. Multi-objectives text clustering method is combined with two evaluating criteria which emerge as a robust alternative in several situations. In particular, the multi-objective function in the text clustering domain is not a popular, and it is a core issue that affects the performance of the text clustering technique. The performance of multi-objectives function is investigated using the k-mean text clustering technique. The experiments were conducted using seven standard text datasets. The results showed that the proposed multi-objectives based method outperforms the other measures in term of the performance of the text clustering, evaluated by using two common clustering measures, namely, Accuracy and F-measure.

Unsupervised feature selection technique based on harmony search algorithm for improving the text clustering

The increasing amount of text information on the Internet web pages affects the clustering analysis. The text clustering is a favorable analysis technique used for partitioning a massive amount of information into clusters. Hence, the major problem that affects the text clustering technique is the presence uninformative and sparse features in text documents. The feature selection (FS) is an important unsupervised technique used to eliminate uninformative features to encourage the text clustering technique. Recently, the meta-heuristic algorithms are successfully applied to solve several optimization problems. In this paper, we proposed the harmony search (HS) algorithm to solve the feature selection problem (FSHSTC). The proposed method is used to enhance the text clustering (TC) technique by obtaining a new subset of informative or useful features. Experiments were applied using four benchmark text datasets. The results show that the proposed FSHSTC is improved the performance of the k-mean clustering algorithm measured by F-measure and Accuracy.

Gene selection for cancer classification by combining minimum redundancy maximum relevancy and bat-inspired algorithm

In this paper, the bat-inspired algorithm (BA) is tolerated to gene selection for cancer classification using microarray datasets. Microarray data consists of irrelevant, redundant, and noisy genes. Gene selection problem is tackled by determining the most informative genes taken from microarray data to accurately diagnose the cancer disease. Gene selection problem is widely solved by optimisation algorithms. BA is a recent swarm-based algorithm, which imitates the echolocation system of bat individuals. It has been successfully applied to several optimisation problems. Gene selection is tackled by combining two stages, namely, filter stage, which uses Minimum Redundancy Maximum Relevancy (MRMR) method; and wrapper stage, which uses BA and SVM. To test the accuracy performance of the proposed method, ten microarray datasets were used. For comparative evaluation, the proposed method was compared with popular gene selection methods. The proposed method achieves comparable results of some datasets and produced new results for one dataset.