Dynamic evolutionary data and text document clustering approach using improved Aquila optimizer based arithmetic optimization algorithm and differential evolution
Data and text clustering are popular and frequently used in the data mining domain, mainly to deal with big data analytics. The main problem in these techniques is finding the most coherent clusters allocating similar-related objects into one group. In this paper, an improved clustering analysis app...
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| Vydané v: | Neural computing & applications Ročník 34; číslo 23; s. 20939 - 20971 |
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| Hlavní autori: | , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
London
Springer London
01.12.2022
Springer Nature B.V |
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| ISSN: | 0941-0643, 1433-3058 |
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| Abstract | Data and text clustering are popular and frequently used in the data mining domain, mainly to deal with big data analytics. The main problem in these techniques is finding the most coherent clusters allocating similar-related objects into one group. In this paper, an improved clustering analysis approach is proposed using an advanced optimization method called AOAOA. The proposed AOAOA method improved the Aquila optimizer (AO) search performance by the operators of the arithmetic optimization algorithms (AOA) and differential evolution (DE) and using a novel transition mechanism. The primary motivation for this modification is that the original optimizer suffers from local optima stagnation and lacks search balance. Thus, the proposed AOAOA overcame these shortcomings by integrating various powerful search strategies and a new update strategy. Experiments are conducted on two parts; eight standard data clustering datasets and ten text documents benchmark datasets to evaluate the performance of the proposed AOAOA method. The proposed method is compared against several well-known optimization algorithms and advanced state-of-the-art methods published in the literature. The data clustering results also showed promising performance for the proposed AOAOA compared to other comparative data clustering methods. Moreover, the results illustrated that the proposed AOAOA can find new best solutions for several different complicated cases as the text document clustering results. The proposed AOAOA got accurate and robust results compared to several state-of-the-art methods. |
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| AbstractList | Data and text clustering are popular and frequently used in the data mining domain, mainly to deal with big data analytics. The main problem in these techniques is finding the most coherent clusters allocating similar-related objects into one group. In this paper, an improved clustering analysis approach is proposed using an advanced optimization method called AOAOA. The proposed AOAOA method improved the Aquila optimizer (AO) search performance by the operators of the arithmetic optimization algorithms (AOA) and differential evolution (DE) and using a novel transition mechanism. The primary motivation for this modification is that the original optimizer suffers from local optima stagnation and lacks search balance. Thus, the proposed AOAOA overcame these shortcomings by integrating various powerful search strategies and a new update strategy. Experiments are conducted on two parts; eight standard data clustering datasets and ten text documents benchmark datasets to evaluate the performance of the proposed AOAOA method. The proposed method is compared against several well-known optimization algorithms and advanced state-of-the-art methods published in the literature. The data clustering results also showed promising performance for the proposed AOAOA compared to other comparative data clustering methods. Moreover, the results illustrated that the proposed AOAOA can find new best solutions for several different complicated cases as the text document clustering results. The proposed AOAOA got accurate and robust results compared to several state-of-the-art methods. |
| Author | Abualigah, Laith Almotairi, Khaled H |
| Author_xml | – sequence: 1 givenname: Laith orcidid: 0000-0002-2203-4549 surname: Abualigah fullname: Abualigah, Laith email: Aligah.2020@gmail.com organization: Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Faculty of Information Technology, Middle East University – sequence: 2 givenname: Khaled H surname: Almotairi fullname: Almotairi, Khaled H organization: Computer Engineering Department, Computer and Information Systems College, Umm Al-Qura University |
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| CitedBy_id | crossref_primary_10_1007_s42979_024_02632_8 crossref_primary_10_1016_j_asoc_2023_110894 crossref_primary_10_1007_s11042_023_17084_0 crossref_primary_10_1016_j_eswa_2024_124823 crossref_primary_10_1007_s00521_023_08983_2 crossref_primary_10_1007_s11831_025_10281_0 crossref_primary_10_1109_ACCESS_2023_3323017 crossref_primary_10_3390_pr11010238 crossref_primary_10_1007_s11831_023_09945_6 crossref_primary_10_1108_ECAM_03_2024_0328 |
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| Keywords | Arithmetic optimization algorithm (AOA) Text document clustering Aquila optimizer (AO) Data clustering Differential evolution (DE) |
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