A New Clustering Method Based on the Inversion Formula

Lukauskas, Mantas; Ruzgas, Tomas

doi:10.3390/math10152559

Cited by 8 publications

(3 citation statements)

References 47 publications

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“…CBMIDE (clustering based on the modified inversion formula density estimation) [71] and RCBMIDE (reduced clustering based on the modified inversion formula density estimation) [72] are novel clustering methods that leverage the modified inversion density estimation to effectively capture the underlying structure of the data. In contrast to the traditional methods such as the Gaussian mixtures models, CBMIDE focuses on the reciprocal distance between the data points and the cluster centers, thereby providing a more robust estimation of the density structure.…”

Section: Clustering Methods Used In the Researchmentioning

confidence: 99%

Enhancing Skills Demand Understanding through Job Ad Segmentation Using NLP and Clustering Techniques

Lukauskas

Šarkauskaitė²,

Pilinkienė

et al. 2023

Applied Sciences

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The labor market has been significantly impacted by the rapidly evolving global landscape, characterized by increased competition, globalization, demographic shifts, and digitization, leading to a demand for new skills and professions. The rapid pace of technological advancements, economic transformations, and changes in workplace practices necessitate that employees continuously adapt to new skill requirements. A quick assessment of these changes enables the identification of skill profiles and the activities of economic fields. This paper aims to utilize natural language processing technologies and data clustering methods to analyze the skill needs of Lithuanian employees, perform a cluster analysis of these skills, and create automated job profiles. The hypothesis that applying natural language processing and clustering in job profile analyzes can allow the real-time assessment of job skill demand changes was investigated. Over five hundred thousand job postings were analyzed to build job/position profiles for further decision-making. In the first stage, data were extracted from the job requirements of entire job advertisement texts. The regex procedure was found to have demonstrated the best results. Data vectorization for initial feature extraction was performed using BERT structure transformers (sentence transformers). Five dimensionality reduction methods were compared, with the UMAP technique producing the best results. The HDBSCAN method proved to be the most effective for clustering, though RCBMIDE also demonstrated a robust performance. Finally, job profile descriptions were generated using generative artificial intelligence based on the compiled job profile skills. Upon expert assessment of the created job profiles and their descriptions, it was concluded that the automated job advertisement analysis algorithm had shown successful results and could therefore be applied in practice.

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Section: Clustering Methods Used In the Researchmentioning

confidence: 99%

Enhancing Skills Demand Understanding through Job Ad Segmentation Using NLP and Clustering Techniques

Lukauskas

Šarkauskaitė²,

Pilinkienė

et al. 2023

Applied Sciences

Self Cite

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“…In recent years, K-means clustering, the Gaussian mixture model (GMM), and mixtures of multivariate clustering algorithms have been applied to the field of chemistry, which has promoted the application of unsupervised learning in other disciplines [18]. With the development of technology, Mantas Lukauskas et al [19] proposed a density clustering method based on improved inverse formula density estimation. The new method has a good effect when dealing with low dimensional data.…”

Section: Optics Clustering Algorithmmentioning

confidence: 99%

Specification Mining Based on the Ordering Points to Identify the Clustering Structure Clustering Algorithm and Model Checking

Fan,

Wang

2024

Algorithms

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Software specifications are of great importance to improve the quality of software. To automatically mine specifications from software systems, some specification mining approaches based on finite-state automatons have been proposed. However, these approaches are inaccurate when dealing with large-scale systems. In order to improve the accuracy of mined specifications, we propose a specification mining approach based on the ordering points to identify the clustering structure clustering algorithm and model checking. In the approach, the neural network model is first used to produce the feature values of states in the traces of the program. Then, according to the feature values, finite-state automatons are generated based on the ordering points to identify the clustering structure clustering algorithm. Further, the finite-state automaton with the highest F-measure is selected. To improve the quality of the finite-state automatons, we refine it based on model checking. The proposed approach was implemented in a tool named MCLSM and experiments, including 13 target classes, were conducted to evaluate its effectiveness. The experimental results show that the average F-measure of finite-state automatons generated by our method reaches 92.19%, which is higher than most related tools.

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“…According to Lukauskas and Ruzgas [ 5 ], regardless of the fact that there are numerous clustering methods, the subject addressed remains as a complex matter. There is a great need for alternate procedures because typical clustering algorithms do not commonly work well with all types of datasets.…”

Section: Introductionmentioning

confidence: 99%

New clusterization of global seaport countries based on their DEA and FDEA network efficiency scores

Nadarajan,

Aruchunan,

Mohd Noor

2024

PLoS ONE

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Global seaport network efficiency can be measured using the Liner Shipping Connectivity Index (LSCI) with Gross Domestic Product. This paper utilizes k-means and hierarchical strategies by leveraging the results obtained from Data Envelopment Analysis (DEA) and Fuzzy Data Envelopment Analysis (FDEA) to cluster 133 countries based on their seaport network efficiency scores. Previous studies have explored hkmeans clustering for traffic, maritime transportation management, swarm optimization, vessel trajectory prediction, vessels behaviours, vehicular ad hoc network etc. However, there remains a notable absence of clustering research specifically addressing the efficiency of global seaport networks. This research proposed hkmeans as the best strategy for the seaport network efficiency clustering where our four newly founded clusters; low connectivity (LC), medium connectivity (MC), high connectivity (HC) and very high connectivity (VHC) are new applications in the field. Using the hkmeans algorithm, 24 countries have been clustered under LC, 47 countries under MC, 40 countries under HC and 22 countries under VHC. With and without a fuzzy dataset distribution, this demonstrates that the hkmeans clustering is consistent and practical to form grouping of general data types. The findings of this research can be useful for researchers, authorities, practitioners and investors in guiding their future analysis, decision and policy makings involving data grouping and prediction especially in the maritime economy and transportation industry.

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A New Clustering Method Based on the Inversion Formula

Cited by 8 publications

References 47 publications

Enhancing Skills Demand Understanding through Job Ad Segmentation Using NLP and Clustering Techniques

Enhancing Skills Demand Understanding through Job Ad Segmentation Using NLP and Clustering Techniques

Specification Mining Based on the Ordering Points to Identify the Clustering Structure Clustering Algorithm and Model Checking

New clusterization of global seaport countries based on their DEA and FDEA network efficiency scores

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