Self-adaptive Frequent Pattern Growth-Based Dynamic Fuzzy Particle Swarm Optimization for Web Document Clustering

Pamba, Raja Varma; Sherly, Elizabeth; Mohan, Kiran

doi:10.1007/978-981-13-1135-2_2

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…In biomedicine, the analysis of individual gene sequences is used to gain an in-depth understanding of different groups, to provide doctors with important reference information to overcome certain diseases [1] [2]. In terms of information retrieval, clustering algorithms can classify and summarize various information, facilitate subsequent maintenance, and help us obtain target information more quickly [3] [4]. In terms of investment finance, through clustering analysis of various related information of different companies, the comprehensive situation of each company is objectively evaluated from the perspective of data, and more reliable suggestions and opinions are provided for investors [5] [6].…”

Section: Introductionmentioning

confidence: 99%

Research on Elastic Network Clustering Algorithm Based on Maximum Entropy

Liu

2021

IJSH

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In the information age, humans need to dig out potential information from a very large amount of information, and cluster analysis is one of the important methods. Most clustering analysis algorithms lack universal applicability, and they often encounter local minima problems when processing data sets with complex and diverse data structures. Cluster analysis has a strong ability to process uncertain information and is very robust. Among them, the elastic network algorithm, which belongs to unsupervised learning, has good geometric properties and can be solved for a specific objective function, which fits well with the definition of the clustering problem. Therefore, this paper has researched based on elastic network algorithm and applied the network in the field of cluster analysis. This paper proposes an Elastic Net of Clustering based on Maximum entropy (ENCM). According to the definition of clustering, the objective function of the elastic network algorithm is changed, and the principle of maximum entropy is used to determine the probability distribution of the data set without prior knowledge. Under the framework of the elastic network, the physical system is simulated, deterministic annealing technology is used to raise and lower the temperature of the system, control the network activity, and use the steepest descent method to track the minimum value.

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Section: Introductionmentioning

confidence: 99%

Research on Elastic Network Clustering Algorithm Based on Maximum Entropy

Liu

2021

IJSH

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Semantics Based Clustering through Cover-Kmeans with OntoVsm for Information Retrieval

Kumar¹,

Kannammal²,

Krishnamoorthy³

et al. 2020

ITC

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Document clustering plays a significant task in the retrieval of the information, which seeks to divide documents into groups automatically, depending on their content similarity. The cluster consists of related documents within the group (having high intra-cluster similarity) and dissimilar to other group documents (having low inter-cluster similarity). Clustering documents should be considered an unsupervised process that aims to classify documents by identifying underlying structures, i.e. the learning process is unsupervised. So there is no need to determine the correct output for an input. Previous clustering methods do not know the semantic associations between words such that the context of documents cannot be correctly interpreted. In order to address this problem, the advent of semantic ontology information such as WordNet was widely used to enhance text clustering consistency. This paper initially proposes an OntoVSM model to reduce the dimension of the document efficiently. The cover K-means clustering algorithm is proposed for semantic document clustering. The proposed algorithm is a hybrid version of K-Means and covers coefficient-based clustering methodology (C3M) that is improved semantically using WordNet ontology. The dimensionality reduction based on semantic knowledge of each term preserves the information without loss. The performance of the proposed work is analysed through experimental results. This shows that the proposed work gives improved results compared to other standard methods.

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Self-adaptive Frequent Pattern Growth-Based Dynamic Fuzzy Particle Swarm Optimization for Web Document Clustering

Cited by 2 publications

References 17 publications

Research on Elastic Network Clustering Algorithm Based on Maximum Entropy

Research on Elastic Network Clustering Algorithm Based on Maximum Entropy

Semantics Based Clustering through Cover-Kmeans with OntoVsm for Information Retrieval

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