An Improved B-hill Climbing Optimization Technique for Solving the Text Documents Clustering Problem
| Autor: | Shishir Kumar Shandilya, Ahamad Tajudin Khader, Essam Said Hanandeh, Laith Abualigah, Mohammed Otair |
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| Rok vydání: | 2020 |
| Předmět: |
Optimization problem
Computer science Datasets as Topic Computational intelligence 02 engineering and technology computer.software_genre Artificial Intelligence 0202 electrical engineering electronic engineering information engineering Cluster Analysis Data Mining Humans Radiology Nuclear Medicine and imaging Local search (optimization) Cluster analysis Mathematical Computing business.industry 020206 networking & telecommunications Document clustering Climbing Benchmark (computing) 020201 artificial intelligence & image processing Data mining business computer Hill climbing Algorithms |
| Zdroj: | Current Medical Imaging Formerly Current Medical Imaging Reviews. 16:296-306 |
| ISSN: | 1573-4056 |
| DOI: | 10.2174/1573405614666180903112541 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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