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Improved Meta-Heuristic Model for text document clustering by Adaptive Weighted Similarity

INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS 2023年第5期31卷 749-771页

作者： Venkanna, Gugulothu Bharati, K. F. Sreenidhi Inst Sci & Technol JNTUA Ananthapuramu Dept Comp Sci & Engn Hyderabad 501301 India JNTUA Coll Engn Autonomous Dept Comp Sci & Engn Ananthapuramu Sir Mokshagundam Vishveshwariah Rd Anantapur 515002 Andhra Pradesh India

This paper intends to develop a novel framework for text document clustering with the aid of a new improved meta-heuristic algorithm. Initially, the features are selected from the text document by subjecting each word under Term Frequency-Inverse document Frequency (TF-IDF) computation. Subsequently, centroid selection plays a vital role in cluster formation, which is done using a new Improved Lion Algorithm (LA) termed as Cross over probability-based LA model (CP-LA). As a novelty, this paper introduced a new inter and intracluster similarity model. Moreover, this centroid selection is made in such a way that the proposed adaptive weighted similarity should be minimal. Based on the characteristics of the document, the weights are automatically adapted with the similarity measure. The proposed adaptive weighted similarity function involves the inter-cluster, and intra-cluster similarity of both ordered and unordered documents. Finally, the superiority of the proposed over other models is proved under different performance measures.

关键词： text document clustering TF-IDF adaptive weight similarity CP-LA model

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Bare-Bones Based Salp Swarm Algorithm for text document clustering

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IEEE ACCESS 2023年 11卷 100010-100028页

作者： Al-Betar, Mohammed Azmi Abasi, Ammar Kamal Al-Naymat, Ghazi Arshad, Kamran Makhadmeh, Sharif Naser Ajman Univ Dept Informat Technol Coll Engn & Informat Technol Ajman U Arab Emirates Ajman Univ Artificial Intelligence Res Ctr AIRC Ajman U Arab Emirates Al Balqa Appl Univ Al Huson Univ Coll Dept Informat Technol Irbid 19117 Jordan Mohamed Bin Zayed Univ Artificial Intelligence MBZ Machine Learning Dept Dept Informat Technol Abu Dhabi U Arab Emirates Univ Petra Dept Data Sci & Artificial Intelligence Amman 11196 Jordan

text document clustering (TDC) is a challenging optimization problem in unsupervised machine learning and text mining. The Salp Swarm Algorithm (SSA) has been found to be effective in solving complex optimization problems. However, the SSA's exploitation phase requires improvement to solve the TDC problem effectively. In this paper, we propose a new approach, known as the Bare-Bones Salp Swarm Algorithm (BBSSA), which leverages Gaussian search equations, inverse hyperbolic cosine control strategies, and greedy selection techniques to create new individuals and guide the population towards solving the TDC problem. We evaluated the performance of the BBSSA on six benchmark datasets from the text clustering domain and six scientific papers datasets extracted from the top eight UAE universities. The experimental results demonstrate that the BBSSA algorithm outperforms traditional SSA and nine other optimization algorithms. Furthermore, the BBSSA algorithm achieves better results than the five traditional clustering techniques.

关键词： Global optimization salp swarm algorithm bare bones greedy selection strategy text document clustering

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text document clustering Using Modified Particle Swarm Optimization with k-means Model

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INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS 2024年第1期33卷 2350061-2350061页

作者： Dodda, Ratnam Babu, A. Suresh Jawaharlal Nehru Technol Univ Dept Comp Sci & Engn Anantapur India

In the present digital era, vast amounts of data are generated by millions of Internet users in the form of unstructured text documents. The clustering and organizing of text documents play a crucial role in the applications of data analysis and market research. In this research manuscript, a new modified version of metaheuristic-based optimization technique is proposed with k-means for clustering the text documents. In the initial phase, the input data are acquired from the three-benchmark databases such as Reuters-21578, 20-Newsgroup and British Broadcasting Corporation (BBC)-sport. Further, the data denoising is accomplished by using the common techniques: stemming, lemmatization, tokenization, and stop word removal. In addition to this, the denoised data are transformed into feature vectors by utilizing Term Frequency (TF)-Inverse document Frequency (IDF) technique. The computed feature vectors are given to the Modified Particle Swarm Optimization (MPSO) with k-means to group the closely related text documents by minimizing the similarity in different clusters. The experimental examination showed that the proposed MPSO with k-means model achieved accuracy of 0.85, 0.85 and 0.86 on the Reuters-21578, 20-Newsgroup and BBC-sport databases, which are superior to the comparative models.

关键词： Dirichlet process k-means clustering machine learning particle swarm optimizer text document clustering

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Dynamic evolutionary data and text document clustering approach using improved Aquila optimizer based arithmetic optimization algorithm and differential evolution

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NEURAL COMPUTING & APPLICATIONS 2022年第23期34卷 20939-20971页

作者： Abualigah, Laith Almotairi, Khaled H. Al Ahliyya Amman Univ Hourani Ctr Appl Sci Res Amman 19328 Jordan Middle East Univ Fac Informat Technol Amman 11831 Jordan Umm Al Qura Univ Comp & Informat Syst Coll Comp Engn Dept Mecca 21955 Saudi Arabia

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.

关键词： Data clustering text document clustering Aquila optimizer (AO) Arithmetic optimization algorithm (AOA) Differential evolution (DE)

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KH-FC: krill herd-based fractional calculus algorithm for text document clustering using MapReduce structure

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INTERNATIONAL JOURNAL OF COMPUTATIONAL SCIENCE AND ENGINEERING 2022年第6期25卷 668-684页

作者： More, Priyanka Shivaprasad Saini, Baljit Singh Lovely Profess Univ Sch Comp Sci & Engn Phagwara Punjab India

In this paper, krill herd-based fractional calculus (KH-FC) using MapReduce framework is proposed for effective text document clustering. Here, the stop word removal and stemming model is applied in the pre-processing step, helps to remove redundant information and hence the size of the information is reduced, which further enhances the clustering accuracy. Furthermore, term frequency (TF) and inverse document frequency (IDF) are employed for extracting significant features. Finally, the developed KH-FC model is utilised for clustering the text documents. The developed KH-FC algorithm is developed by combining the FC concept into the KH technique. In this method, pre-processing and feature extraction is performed in the mapper phase, whereas the clustering process is executed in the reducer phase. The performance of the developed approach is evaluated based on performance metrics, like accuracy, Jaccard coefficient, and F-measure. The developed KH-FC approach obtained better performance in terms of accuracy, Jaccard coefficient, and F-measure is 0.983, 0.936 and 0.967, respectively.

关键词： text document clustering fraction calculus krill herd algorithm Jaccard similarity term frequency-inverse document frequency IDF

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Optimal text document clustering Enabled by Weighed Similarity Oriented Jaya With Grey Wolf Optimization Algorithm

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COMPUTER JOURNAL 2021年第6期64卷 960-972页

作者： Venkanna, Gugulothu Bharati, K. F. Jawaharlal Nehru Technol Univ Dept Comp Sci & Engn Anantapur 515002 Andhra Pradesh India Jawaharlal Nehru Technol Univ Anantapur 515002 Andhra Pradesh India

Owing to scientific development, a variety of challenges present in the field of information retrieval. These challenges are because of the increased usage of large volumes of data. These huge amounts of data are presented from large-scale distributed networks. Centralization of these data to carry out analysis is tricky. There exists a requirement for novel text document clustering algorithms, which overcomes challenges in clustering. The two most important challenges in clustering are clustering accuracy and quality. For this reason, this paper intends to present an ideal clustering model for text document using term frequency-inverse document frequency, which is considered as feature sets. Here, the initial centroid selection is much concentrated which can automatically cluster the text using weighted similarity measure in the proposed clustering process. In fact, the weighted similarity function involves the inter-cluster, and intra-cluster similarity of both ordered and unordered documents, which is used to minimize weighted similarity among the documents. An advanced model for clustering is proposed by the hybrid optimization algorithm, which is the combination of the Jaya Algorithm (JA) and Grey Wolf Algorithm (GWO), and so the proposed algorithm is termed as JA-based GWO. Finally, the performance of the proposed model is verified through a comparative analysis with the state-of-the-art models. The performance analysis exhibits that the proposed model is 96.56% better than genetic algorithm, 99.46% better than particle swarm optimization, 97.09% superior to Dragonfly algorithm, and 96.21% better than JA for the similarity index. Therefore, the proposed model has confirmed its efficiency through valuable analysis.

关键词： text document clustering term frequency inverse document frequency Jaya algorithm Grey Wolf algorithm hybrid concept

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Dynamic Sub-Swarm Approach of PSO Algorithms for text document clustering

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SENSORS 2022年第24期22卷 9653页

作者： Selvaraj, Suganya Choi, Eunmi Kookmin Univ Dept Financial Informat Secur Seoul 02707 South Korea Kookmin Univ Coll Comp Sci Dept Software Seoul 02707 South Korea Seoul Natl Univ SNU Future Innovat Inst Siheung Campus Shihung 15011 South Korea

text document clustering is one of the data mining techniques used in many real-world applications such as information retrieval from IoT Sensors data, duplicate content detection, and document organization. Swarm intelligence (SI) algorithms are suitable for solving complex text document clustering problems compared to traditional clustering algorithms. The previous studies show that in SI algorithms, particle swarm optimization (PSO) provides an effective solution to text document clustering problems. This PSO still needs to be improved to avoid the problems such as premature convergence to local optima. In this paper, an approach called dynamic sub-swarm of PSO (subswarm-PSO) is proposed to improve the results of PSO for text document clustering problems and avoid the local optimum by improving the global search capabilities of PSO. The results of this proposed approach were compared with the standard PSO algorithm and K-means algorithm. As for performance assurance, the evaluation metric purity is used with six benchmark data sets. The experimental results of this study show that our proposed subswarm-PSO algorithm performs best with high purity comparing the standard PSO and K-means traditional algorithms and also the execution time of subswarm-PSO comparatively takes a little less than the standard PSO algorithm.

关键词： swarm intelligence particle swarm optimization sub-swarm PSO text document clustering

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A Grey Wolf Optimizer for text document clustering

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JOURNAL OF INTELLIGENT SYSTEMS 2020年第1期29卷 814-830页

作者： Rashaideh, Hasan Sawaie, Ahmad Al-Betar, Mohammed Azmi Abualigah, Laith Mohammad Al-laham, Mohammad M. Al-Khatib, Ra'ed M. Braik, Malik Al Balqa Appl Univ BAU PABG Fac Informat Technol Comp Sci Dept POB 19117 Salt Jordan Al Balqa Appl Univ BAU Dept Informat Technol Al Huson Univ Coll POB 50 Irbid Jordan Amman Arab Univ Fac Comp Sci & Informat Amman 11953 Jordan Al Balqa Appl Univ BAU Dept Management Informat Syst Amman Univ Coll Amman Jordan YU Dept Comp Sci Fac Informat Technol & Comp Sci Irbid Jordan

text clustering problem (TCP) is a leading process in many key areas such as information retrieval, text mining, and natural language processing. This presents the need for a potent document clustering algorithm that can be used effectively to navigate, summarize, and arrange information to congregate large data sets. This paper encompasses an adaptation of the grey wolf optimizer (GWO) for TCP, referred to as TCP-GWO. The TCP demands a degree of accuracy beyond that which is possible with metaheuristic swarm-based algorithms. The main issue to be addressed is how to split text documents on the basis of GWO into homogeneous clusters that are sufficiently precise and functional. Specifically, TCP-GWO, or referred to as the document clustering algorithm, used the average distance of documents to the cluster centroid (ADDC) as an objective function to repeatedly optimize the distance between the clusters of the documents. The accuracy and efficiency of the proposed TCP-GWO was demonstrated on a sufficiently large number of documents of variable sizes, documents that were randomly selected from a set of six publicly available data sets. documents of high complexity were also included in the evaluation process to assess the recall detection rate of the document clustering algorithm. The experimental results for a test set of over a part of 1300 documents showed that failure to correctly cluster a document occurred in less than 20% of cases with a recall rate of more than 65% for a highly complex data set. The high F-measure rate and ability to cluster documents in an effective manner are important advances resulting from this research. The proposed TCP-GWO method was compared to the other well-established text clustering methods using randomly selected data sets. Interestingly, TCP-GWO outperforms the comparative methods in terms of precision, recall, and F-measure rates. In a nutshell, the results illustrate that the proposed TCP-GWO is able to excel compared to the oth

关键词： text document clustering optimization metaheuristic grey wolf optimizer swarm intelligence

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Swarm Intelligence Algorithms in text document clustering with Various Benchmarks

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SENSORS 2021年第9期21卷 3196-3196页

作者： Selvaraj, Suganya Choi, Eunmi Kookmin Univ Dept Financial Informat Secur Seoul 02707 South Korea Kookmin Univ Dept Software Coll Comp Sci Seoul 02707 South Korea

text document clustering refers to the unsupervised classification of textual documents into clusters based on content similarity and can be applied in applications such as search optimization and extracting hidden information from data generated by IoT sensors. Swarm intelligence (SI) algorithms use stochastic and heuristic principles that include simple and unintelligent individuals that follow some simple rules to accomplish very complex tasks. By mapping features of problems to parameters of SI algorithms, SI algorithms can achieve solutions in a flexible, robust, decentralized, and self-organized manner. Compared to traditional clustering algorithms, these solving mechanisms make swarm algorithms suitable for resolving complex document clustering problems. However, each SI algorithm shows a different performance based on its own strengths and weaknesses. In this paper, to find the best performing SI algorithm in text document clustering, we performed a comparative study for the PSO, bat, grey wolf optimization (GWO), and K-means algorithms using six data sets of various sizes, which were created from BBC Sport news and 20 newsgroups. Based on our experimental results, we discuss the features of a document clustering problem with the nature of SI algorithms and conclude that the PSO and GWO SI algorithms are better than K-means, and among those algorithms, the PSO performs best in terms of finding the optimal solution.

关键词： swarm intelligence algorithms text document clustering artificial intelligence data mining

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

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2018年 73卷 111-125页

作者： Abualigah, Laith Mohammad Khader, Ahamad Tajudin Hanandeh, Essam Said Univ Sains Malaysia Sch Comp Sci George Town 11800 Malaysia Zarqa Univ Dept Comp Informat Syst POB 13132 Zarqa Jordan

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.

关键词： Combination of objective functions Hybridization Krill herd algorithm K-mean algorithm text document clustering

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