A New Word Clustering Algorithm Based on Word Similarity

Yuan, Lichi

doi:10.1049/cje.2017.09.016

Cited by 9 publications

(3 citation statements)

References 5 publications

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“…It can be seen that every metric space is also a topological space, but the opposite is inaccurate. That is, there is such a topological space, which cannot be made into a metric space The remainder is a set {x | x ∈ X and x ∈/N}, expressed as X | N. Point X is called the boundary point of set N. If it is neither the interior point of set N nor the interior point of its coset X | N., the set of all boundary points is called the boundary of set N, which is recorded as LN [7].…”

Section: The Theoretical Basis Of Point Set Topologymentioning

confidence: 99%

Formal topological description of three-dimensional topological entities based on k-dimensional pseudo-manifold

Zhao

2023

TNS

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This paper clarifies the necessity of studying three-dimensional spatial data models, analyzes the research progress and existing problems of topological spatial relationship description methods, and proposes a complete and formal description framework of three-dimensional topological spatial relations based on point set topological theory and dimension expansion method. On this basis, the topological spatial relations existing in three-dimensional spatial objects are classified, and five fundamental topological spatial relations are defined. The mutual exclusivity and completeness of the minimal set of 3D topological space relations are proved. Based on point set topology and k-dimensional pseudo-manifold, this paper proposes the formal description of three-dimensional topological properties and spatial relations between spatial entities. The simplicial homology is applied to manifolds, the topological properties of three-dimensional space entities are revealed, and the formal description method of three-dimensional space entities is given through the geometric structural relationship between simplex and simplicial k-complex.

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Section: The Theoretical Basis Of Point Set Topologymentioning

confidence: 99%

Formal topological description of three-dimensional topological entities based on k-dimensional pseudo-manifold

Zhao

2023

TNS

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“…Further work huge short text data sets will be collecting as real data sets. Caiyan jia proposed short text clustering is a sparsity problem of text data (37)(38)(39)(40).…”

Section: Topic Modelling In Text Analyticsmentioning

confidence: 99%

A Short Text Clustering Approaches in Social Media

Vinoth¹,

Prabhavathy²

2022

ECS Trans.

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In general, handling bag-of-words representation or TF-IDF is an arduous task with short text clustering as it results in sparse vector representation mainly for short texts. This paper provides an in-depth study on learning predictor features through an auto encoder model and sentence embedding technique. Assignment of user from any clustering technique as a process of supervision in order to update the weight of the encoder analysis. Short text related datasets validate of measure effective methods and algorithms for short text unsupervised data. Among this short text are not able to clustering in some social media. This paper discusses the challenge and methods to cluster in social media short text challenging issues. Clustering methods of algorithms to sort the problem to recovery the short text using K-means algorithms of Convolution Neural Network method. This paper provides a comprehensive review of short text prediction using the clustering algorithm, explore the research challenges, and open issues in this area.

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“…In recent years, deep learning, one of the most popular topics in artificial intelligence, has been constantly stimulating new revolutions in both the academic and industrial communities. By virtue of increasingly powerful hardware calculation abilities and the tremendous amount of collected datasets, deep learning has kept refreshing records and creating breaking news in areas such as machine learning [14] , image processing [15] , natural language processing [16] , and speech recognition [17] . Deep neural network provides another way of providing the embedded features in lower-dimensional representations [18,19] .…”

Section: Introductionmentioning

confidence: 99%