Sophisticated SOM based genetic operators in multi-objective clustering framework

Saini, Naveen; Saha, Sriparna; Harsh, Aditya; Bhattacharyya, Pushpak

doi:10.1007/s10489-018-1350-8

Cited by 11 publications

(3 citation statements)

References 29 publications

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“…Another MOC algorithm, SOMDEA-clust [31], proposes an efficient automated decomposition-based multi-objective clustering technique, which is a hybridization of Self-Organizing Maps (SOM) and differential evolution algorithm. Two internal cluster validity indices, namely, Silhouette index (SI) and PBM (Pakhira-Bandyopadhyay-Maulik) index, are used as objective functions.…”

Section: Evolutionary Multi-objective Clustering Methods (Moc)mentioning

confidence: 99%

Improved Multi-objective Data Stream Clustering with Time and Memory Optimization

Attaoui¹,

Azzag²,

Lebbah³

et al. 2022

Preprint

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The analysis of data streams has received considerable attention over the past few decades due to sensors, social media, etc. It aims to recognize patterns in an unordered, infinite, and evolving stream of observations. Clustering this type of data requires some restrictions in time and memory. This paper introduces a new data stream clustering method (IMOC-Stream). This method, unlike the other clustering algorithms, uses two different objective functions to capture different aspects of the data. The goal of IMOC-Stream is to: 1) reduce computation time by using idle times to apply genetic operations and enhance the solution. 2) reduce memory allocation by introducing a new tree synopsis. 3) find arbitrarily shaped clusters by using a multiobjective framework. We conducted an experimental study with high dimensional stream datasets and compared them to well-known stream clustering techniques. The experiments show the ability of our method to partition the data stream in arbitrarily shaped, compact, and well-separated clusters while optimizing the time and memory. Our method also outperformed most of the stream algorithms in terms of NMI and ARAND measures.

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Section: Evolutionary Multi-objective Clustering Methods (Moc)mentioning

confidence: 99%

Improved Multi-objective Data Stream Clustering with Time and Memory Optimization

Attaoui¹,

Azzag²,

Lebbah³

et al. 2022

Preprint

View full text Add to dashboard Cite

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“…The main TAs utilized for TDC are K-means and K-medoids. Other TAs used for TDC are self-organizing maps (som) [10], and β-hill climbing.…”

Section: Introductionmentioning

confidence: 99%

Text documents clustering using modified multi-verse optimizer

Abasi

Khader

Al-Betar

et al. 2020

IJECE

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In this study, a multi-verse optimizer (MVO) is utilised for the text document clus- tering (TDC) problem. TDC is treated as a discrete optimization problem, and an objective function based on the Euclidean distance is applied as similarity measure. TDC is tackled by the division of the documents into clusters; documents belonging to the same cluster are similar, whereas those belonging to different clusters are dissimilar. MVO, which is a recent metaheuristic optimization algorithm established for continuous optimization problems, can intelligently navigate different areas in the search space and search deeply in each area using a particular learning mechanism. The proposed algorithm is called MVOTDC, and it adopts the convergence behaviour of MVO operators to deal with discrete, rather than continuous, optimization problems. For evaluating MVOTDC, a comprehensive comparative study is conducted on six text document datasets with various numbers of documents and clusters. The quality of the ﬁnal results is assessed using precision, recall, F-measure, entropy accuracy, and purity measures. Experimental results reveal that the proposed method performs competitively in comparison with state-of-the-art algorithms. Statistical analysis is also conducted and shows that MVOTDC can produce signiﬁcant results in comparison with three well-established methods.

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“…MOBDE shows superior performance [32–34] over the existing evolutionary algorithms like NSGA-II [14], MOEA/D [35], etc. Moreover, researchers have shown effectiveness of DE in solving different real-life optimization problems like clustering [36–41], summarization [42–44]. It was shown in the literature that DE also outperforms [18, 45, 46] Particle Swarm Optimization (PSO) [47] which is another optimization strategy.…”

Section: Introductionmentioning

confidence: 99%

Extractive single document summarization using binary differential evolution: Optimization of different sentence quality measures

et al. 2019

Self Cite

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With the increase in the amount of text information in different real-life applications, automatic text-summarization systems become more predominant in extracting relevant information. In the current study, we formulated the problem of extractive text-summarization as a binary optimization problem, and multi-objective binary differential evolution (DE) based optimization strategy is employed to solve this. The solutions of DE encode a possible subset of sentences to be present in the summary which is then evaluated based on some statistical features (objective functions) namely, the position of the sentence in the document, the similarity of a sentence with the title, length of the sentence, cohesion, readability, and coverage. These objective functions, measuring different aspects of summary, are optimized simultaneously using the search capability of DE. Some newly designed self-organizing map (SOM) based genetic operators are incorporated in the optimization process to improve the convergence. SOM generates a mating pool containing solutions and their neighborhoods. This mating pool takes part in the genetic operation (crossover and mutation) to create new solutions. To measure the similarity or dissimilarity between sentences, different existing measures like normalized Google distance, word mover distance, and cosine similarity are explored. For the purpose of evaluation, two standard summarization datasets namely, DUC2001, and DUC2002 are utilized, and the obtained results are compared with various supervised, unsupervised and optimization strategy based existing summarization techniques using ROUGE measures. Results illustrate the superiority of our approach in terms of convergence rate and ROUGE scores as compared to state-of-the-art methods. We have obtained 45% and 5% improvements over two recent state-of-the-art methods considering ROUGE−2 and ROUGE−1 scores, respectively, for the DUC2001 dataset. While for the DUC2002 dataset, improvements obtained by our approach are 20% and 5%, considering ROUGE−2 and ROUGE−1 scores, respectively. In addition to these standard datasets, CNN news dataset is also utilized to evaluate the efficacy of our proposed approach. It was also shown that the best performance not only depends on the objective functions used but also on the correct choice of similarity/dissimilarity measure between sentences.

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Sophisticated SOM based genetic operators in multi-objective clustering framework

Cited by 11 publications

References 29 publications

Improved Multi-objective Data Stream Clustering with Time and Memory Optimization

Improved Multi-objective Data Stream Clustering with Time and Memory Optimization

Text documents clustering using modified multi-verse optimizer

Extractive single document summarization using binary differential evolution: Optimization of different sentence quality measures

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