Essam Said Hanandeh

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 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.

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

Text clustering is an efficient analysis technique used in the domain of the text mining to arrange a huge of unorganized text documents into a subset of coherent clusters. Where, the similar documents in the same cluster. In this paper, we proposed a novel term weighting scheme, namely, length feature weight (LFW), to improve the text document clustering algorithms based on new factors. The proposed scheme assigns a favorable term weight according to the obtained information from the documents collection. It recognizes the terms which are particular to each cluster and enhances their weights based on the proposed factors at the level of the document. β-hill climbing technique is used to validate the proposed scheme in the text clustering. The proposed weight scheme is compared with the existing weight scheme (TF-IDF) to validate its results in that domain. Experiments are conducted on eight standard benchmark text datasets taken from the Laboratory of Computational Intelligence (LABIC). The results proved that the proposed weighting scheme LFW overcomes the existing weighting scheme and enhances the result of text document clustering technique in terms of the F-measure, precision, and recall.

A combination of objective functions and hybrid Krill herd algorithm for text document clustering analysis

Abstract Krill herd (KH) algorithm is a novel swarm-based optimization algorithm that imitates krill herding behavior during the searching for foods. It has been successfully used in solving many complex optimization problems. The potency of this algorithm is very high because of its superior performance compared with other optimization algorithms. Hence, the applicability of this algorithm for text document clustering is investigated in this work. Text document clustering refers to the method of clustering an enormous amount of text documents into coherent and dense clusters, where documents in the same cluster are similar. In this paper, a combination of objective functions and hybrid KH algorithm, called, MHKHA, is proposed to solve the text document clustering problem. In this version, the initial solutions of the KH algorithm are inherited from the k-mean clustering algorithm and the clustering decision is based on two combined objective functions. Nine text standard datasets collected from the Laboratory of Computational Intelligence are used to evaluate the performance of the proposed algorithms. Five evaluation measures are employed, namely, accuracy, precision, recall, F-measure, and convergence behavior. The proposed versions of the KH algorithm are compared with other well-known clustering algorithms and other thirteen published algorithms in the literature. The MHKHA obtained the best results for all evaluation measures and datasets used among all the clustering algorithms tested.

Hybrid clustering analysis using improved krill herd algorithm

In this paper, a novel text clustering method, improved krill herd algorithm with a hybrid function, called MMKHA, is proposed as an efficient clustering way to obtain promising and precise results in this domain. Krill herd is a new swarm-based optimization algorithm that imitates the behavior of a group of live krill. The potential of this algorithm is high because it performs better than other optimization methods; it balances the process of exploration and exploitation by complementing the strength of local nearby searching and global wide-range searching. Text clustering is the process of grouping significant amounts of text documents into coherent clusters in which documents in the same cluster are relevant. For the purpose of the experiments, six versions are thoroughly investigated to determine the best version for solving the text clustering. Eight benchmark text datasets are used for the evaluation process available at the Laboratory of Computational Intelligence (LABIC). Seven evaluation measures are utilized to validate the proposed algorithms, namely, ASDC, accuracy, precision, recall, F-measure, purity, and entropy. The proposed algorithms are compared with the other successful algorithms published in the literature. The results proved that the proposed improved krill herd algorithm with hybrid function achieved almost all the best results for all datasets in comparison with the other comparative algorithms.

Feature Selection with β-Hill Climbing Search for Text Clustering Application

In the bases of increasing the volume of text information, the dealing with text information has become incredibly complicated. The text clustering is a suitable technique used in dealing with a tremendous amount of text documents by classifying these set of text documents into clusters. Ultimately, text documents hold sparse, non-uniform distribution and uninformative features are difficult to cluster. The text feature selection is a primary unsupervised learning method that is utilized to choose a new subset of informational text features. In this paper, a new algorithm is proposed based on β-hill climbing technique for text feature selection problem to improve the text clustering (B-FSTC). The results of the proposed method for β-hill climbing and original Hill climbing (i.e., H-FSTC) are examined using the k-mean text clustering and compared with each other. Experiments were conducted on four standard text datasets with varying characteristics. Interestingly, the proposed β-hill climbing algorithm obtains superior results in comparison with the other well-regard techniques by producing a new subset of informational text features. Lastly, the β-hill climbing-based feature selection method supports the k-mean clustering algorithm to achieve more precise clusters.

Modified Krill Herd Algorithm for Global Numerical Optimization Problems

For the purpose of improving the search strategy of the krill herd algorithm (KHA) , an improved robust approach is proposed to address the function optimization problems, namely, modified krill herd algorithm (MKHA) . In MKHA method, the modification of krill herd algorithm focuses on genetic operators (GOs) and it occurs in the ordering of procedures of the basic krill herd algorithm, where the crossover and mutation operators are employed after the updating process of the krill individuals position, the krill herd (KH) motion calculations, is finished. This modification is conducted because the genetic operators insignificantly exploit to enhance the global exploration search in the basic krill herd algorithm so as to speed up convergence. Several versions of benchmark functions are applied to verify the proposed method (MKHA) and it is showed that, in most cases, the proposed algorithm (MKHA) obtained better results in comparison with the basic KHA and other comparative methods.

An improved B-hill climbing optimization technique for solving the text documents clustering problem

BACKGROUND Considering the increasing volume of text document information on Internet pages, dealing with such a tremendous amount of knowledge becomes totally complex due to its large size. Text clustering is a common optimization problem used to manage a large amount of text information into a subset of comparable and coherent clusters. AIMS This paper presents a novel local clustering technique, namely, β-hill climbing, to solve the problem of the text document clustering through modeling the β-hill climbing technique for partitioning the similar documents into the same cluster. METHODS The β parameter is the primary innovation in β-hill climbing technique. It has been introduced in order to perform a balance between local and global search. Local search methods are successfully applied to solve the problem of the text document clustering such as; k-medoid and kmean techniques. RESULTS Experiments were conducted on eight benchmark standard text datasets with different characteristics taken from the Laboratory of Computational Intelligence (LABIC). The results proved that the proposed β-hill climbing achieved better results in comparison with the original hill climbing technique in solving the text clustering problem. CONCLUSION The performance of the text clustering is useful by adding the β operator to the hill climbing.

Evaluating and enhancing e-learning in Jordan

One of the important facilities offered by the internet is the e-learning, which represents a major change in the educational environment. It is considered as a source of services for educational organisations and users. For enhancing the effectiveness of e-learning, it is necessary to make major changes in the educational operational aspects along with constructing potential technology. The aims of this paper are to examine the users satisfaction of the e-learning services, and to measure the degree of confidence in the services provided by the organisation.