Computational Intelligence in Clustering Algorithms, With Applications

Xu, Rui; Wunsch, Donald C.

doi:10.1007/978-3-540-46551-5_3

Cited by 6 publications

(2 citation statements)

References 81 publications

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“…56 Euclidean similarity measures the magnitude (frequency) over the direction (semantic) of word vectors, and it can maximize the intercluster distance comparing to cosine similarity. 48,57 Agglomerative (bottom-up) algorithm treats each document as a singleton cluster at the beginning, then successively combines nearby pair of document clusters into one cluster. Eventually, all clusters will be merged into one big cluster, which contains all of the documents.…”

Section: Resultsmentioning

confidence: 99%

Representing Multiword Chemical Terms through Phrase-Level Preprocessing and Word Embedding

Huang¹,

Ling²

2019

ACS Omega

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In recent years, data-driven methods and artificial intelligence have been widely used in chemoinformatic and material informatics domains, for which the success is critically determined by the availability of training data with good quality and large quantity. A potential approach to break this bottleneck is by leveraging the chemical literature such as papers and patents as alternative data resources to high throughput experiments and simulation. Compared to other domains where natural language processing techniques have established successes, the chemical literature contains a large portion of phrases of multiple words that create additional challenges for accurate identification and representation. Here, we introduce a chemistry domain suitable approach to identify multiword chemical terms and train word representations at the phrase level. Through a series of special-designed experiments, we demonstrate that our multiword identifying and representing method effectively and accurately identifies multiword chemical terms from 119, 166 chemical patents and is more robust and precise to preserve the semantic meaning of chemical phrases compared to the conventional approach, which represents constituent single words first and combine them afterward. Because the accurate representation of chemical terms is the first and essential step to provide learning features for downstream natural language processing tasks, our results pave the road to utilize the large volume of chemical literature in future data-driven studies.

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Section: Resultsmentioning

confidence: 99%

Representing Multiword Chemical Terms through Phrase-Level Preprocessing and Word Embedding

Huang¹,

Ling²

2019

ACS Omega

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“…where C ----Number of clusters; N ----Total number of samples; M ----Total number of features; U = [u ik ] ----The degree of membership of the k-th class on the i-th sample; V = [v kj ] ----The center value of the k-th class on the j-th dimension; W =[w kj ] ----Feature weight value of the k-th class on the j-th dimension feature; x ij ----The value of the i-th sample on the j-th feature; k𝜎 = exp(-(v kj -v oj ) 2 / 𝜎 2 ) ----Gaussian kernel function, which changes the original measurement method to a certain extent; 𝜎 ----Parameter, the value of this article is 2, (𝜎 = [2, 5, 10]) [34]; γ ----Information entropy coefficient, which is used to coordinate the influence of entropy on the clustering results, the value range is (0,1); 𝜂 ----Reciprocal of sample data variance, such as…”

Section: Multi-objective Mathematical Modelmentioning

confidence: 99%

Novel algorithm for identifying kinematics characteristic data under road conditions in Xi’an

Wang¹,

Hu²,

Zhao³

et al. 2023

Third International Symposium on Computer Engineering and Intelligent Communications (ISCEIC 2022)

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Aiming at the problem that the kinematics feature dataset of different traffic flow, average speed, acceleration, and traffic time are affected by high-dimensional, irrelevant, and redundant factors, and the kinematics feature dataset is a multiobjective, multi-constrained and complex nonlinear optimization system, the improved multi-objective evolutionary soft subspace clustering algorithm (iMOSSC) is proposed to mine the micro-stroke segments with different kinematic characteristics and realize data classification. The algorithm uses iNSGA-II as the base algorithm and performs local search operator and repair operator operation in the feature space to accelerate convergence and improve the accuracy of the solution. The feasibility and effectiveness of the algorithm are verified by 12 sets of UCI standard dataset. The classified kinematics characteristic data is used to construct the Xi'an urban road trajectory database. Compared with the iMWK-HD algorithm in the collected kinematics feature data of circulation condition, the feature importance degree of the iMOSSC algorithm is more reasonable, the stability is better, the accuracy is higher, and the classification effectiveness is more obvious than the iMWK-HD algorithm. The excavated kinematics data is imported into the Optimumlap simulation software to construct the actual road circulation condition trajectory database. Based on the ADVISOR commercial software platform for the simulation module. The simulation results show that the fuel economy, acceleration time, and gradeability of the novel dual-mode&dual-motor hybrid drive system are better than those of gasoline vehicles and Prius vehicles when operating under actual road conditions.

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Computational Framework for Generating Visual Summaries of Topical Clusters in Twitter Streams

Kas

Suh

2013

Social Networks: A Framework of Computational Intelligence

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Computational Intelligence in Clustering Algorithms, With Applications

Cited by 6 publications

References 81 publications

Representing Multiword Chemical Terms through Phrase-Level Preprocessing and Word Embedding

Representing Multiword Chemical Terms through Phrase-Level Preprocessing and Word Embedding

Novel algorithm for identifying kinematics characteristic data under road conditions in Xi’an

Computational Framework for Generating Visual Summaries of Topical Clusters in Twitter Streams

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