Proceedings ArticleDOI
A krill herd algorithm for efficient text documents clustering
Laith Abualigah,Ahamad Tajudin Khader,Mohammed Azmi Al-Betar,Mohammed A. Awadallah +3 more
- pp 67-72
TLDR
Two novel text clustering algorithms based on krill herd (KH) algorithm are proposed to improve the web text documents clustering by outperforming the k-mean algorithm in term of clusters quality that is evaluated using two common clustering measures, namely, Purity and Entropy.Abstract:
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.read more
Citations
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Journal ArticleDOI
A new feature selection method to improve the document clustering using particle swarm optimization algorithm
TL;DR: 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 that can improve the performance of the text clustering technique and reduce the computational time.
Journal ArticleDOI
Unsupervised text feature selection technique based on hybrid particle swarm optimization algorithm with genetic operators for the text clustering
TL;DR: 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, and is compared with the other comparative algorithms published in the literature.
Journal ArticleDOI
Hybrid clustering analysis using improved krill herd algorithm
TL;DR: 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.
Journal ArticleDOI
A combination of objective functions and hybrid Krill herd algorithm for text document clustering analysis
TL;DR: A combination of objective functions and hybrid KH algorithm, called, MHKHA, is proposed to solve the text document clustering problem and obtained the best results for all evaluation measures and datasets used among all the clustering algorithms tested.
Journal ArticleDOI
A novel hybridization strategy for krill herd algorithm applied to clustering techniques
TL;DR: The experiments reveal that the proposed hybrid KHA with HS algorithm (H-KHA) is superior or at least highly competitive with the original KH algorithm, well-known clustering techniques and other comparative optimization algorithms.
References
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Journal ArticleDOI
Krill herd: A new bio-inspired optimization algorithm
Amir H. Gandomi,Amir H. Alavi +1 more
TL;DR: The proposed KH algorithm, based on the simulation of the herding behavior of krill individuals, is capable of efficiently solving a wide range of benchmark optimization problems and outperforms the exciting algorithms.
Journal ArticleDOI
A survey on nature inspired metaheuristic algorithms for partitional clustering
TL;DR: An up-to-date review of all major nature inspired metaheuristic algorithms employed till date for partitional clustering and key issues involved during formulation of various metaheuristics as a clustering problem and major application areas are discussed.
Proceedings ArticleDOI
Document clustering using particle swarm optimization
TL;DR: This paper presents a particle swarm optimization (PSO) document clustering algorithm, which performs a globalized search in the entire solution space and shows that the hybrid PSO algorithm can generate more compact clustering results than the K-means algorithm.
Journal ArticleDOI
Applying genetic algorithms to information retrieval using vector space model
TL;DR: The researcher explored the problems embedded in this process, attempted to find solutions such as the way of choosing mutation probability and fitness function, and chose Cranfield English Corpus test collection on mathematics, and concluded that the authors might have several improvements when using adaptive genetic algorithms.
Journal ArticleDOI
Economic load dispatch using krill herd algorithm
TL;DR: In this article, a new and efficient krill herd algorithm (KHA) was proposed to solve both convex and non-convex ELD problems of thermal power units considering valve point loading, multiple fuel operation, transmission losses and constraints such as ramp rate limits and prohibited operating zones.