Agglomerative Hierarchical Clustering for Information Retrieval Using Latent Semantic Index

Park, Han-Saem; Kwon, Kyunglag; Khiati, Abdel-ilah Zakaria; Lee, Jeungmin; Chung, In-Jeong

doi:10.1109/smartcity.2015.108

Cited by 10 publications

(6 citation statements)

References 21 publications

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“…An agglomerative hierarchical clustering that has a down-to-top operation shows a better performance for the data structure. Web clustering has become a topic for researchers in the field of information retrieval for many years [29]. Aggomolative hierarchical clustering is often used more than divisive in data retrieval [30].…”

Section: Comparison and Selection Of Appropriate Clusteringmentioning

confidence: 99%

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Text-based multi-dimensional medical images retrieval according to the features-usage correlation

Safaei

2021

Med Biol Eng Comput

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Section: Comparison and Selection Of Appropriate Clusteringmentioning

confidence: 99%

“…The reason for choosing Aggomolative hierarchical clustering as the priority over non-hierarchical clustering algorithms in retrieval is as follows [29]:…”

Section: Comparison and Selection Of Appropriate Clusteringmentioning

confidence: 99%

Text-based multi-dimensional medical images retrieval according to the features-usage correlation

Safaei

2021

Med Biol Eng Comput

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“…However, some well-known disadvantages are the noise induced by the snippets and the complexity. Here the SRC problems such as synonymy, polysemy, flat clustering were analysed and discussed [3]. Another well-known SRC algorithm named Lingo known by the-description-comes-first approach, the main idea of approach is that they proceed by generating meaningful labels to the clusters and assign each snippet to the right cluster.…”

Section: Literature Surveymentioning

confidence: 99%

Advising Projects to Students using Data Mining and Natural Language Processing

Solim¹,

Sagwekar²,

Patil³

et al. 2017

IJARCSSE

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I. INTRODUCTIONMost of the students have to do various projects in their curriculum. For number of subjects and fields, they need to select many project topics. These projects topics are required to have research papers associated with it. These papers give some idea to students for design, analysis, implementation and testing. Sometimes, they just have idea about domain in which they want to work. Due to large number of dataset on research paper's website, it is very time consuming task to find research papers which have work on similar problem with different technique to solve that problem. With 'Project Advisor', students can select field of their domain of interest and list of problems in that domain with list of related research papers will be shown as an output to students. This system is split into four phases-scrapping, classification, clustering, natural language processing. In the first phase of our system crawl through the web pages of research papers website to scrap title, abstract and various types of keywords. In the classification phase, it classifies papers into preselected four domains. The clusters of research papers with similar topics are found. In the final phase, a problem statement is generated for each cluster based on title and keywords. The input data is called training data and has a known label or result such as health-care or security at a time. A model is prepared through a training process where it is required to make predictions and is corrected when those predictions are wrong. Thus the system is trained till the model attains a required accuracy on the training data.

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“…The AHC algorithm with TF-IDF has been used to cluster the web pages [17], construct taxonomies from a corpus of text documents [18], construct multi-keyword ranked search scheme [19], context aware document clustering [20], automatic taxonomy construction from keywords [21]. The AHC algorithm has also been developed with LSI for document clustering [22], clustering of news articles [23], information retrieval [24]. The weakness of LSI is overcome by developing a topic-based weighting term called Latent Dirichlet allocation (LDA).…”

Section: Introductionmentioning

confidence: 99%

Complete agglomerative hierarchy document’s clustering based on fuzzy luhn’s gibbs latent dirichlet allocation

Prihatini¹,

Putra²,

Giriantari³

et al. 2019

IJECE

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Agglomerative hierarchical is a bottom up clustering method, where the distances between documents can be retrieved by extracting feature values using a topic-based latent dirichlet allocation method. To reduce the number of features, term selection can be done using Luhn's Idea. Those methods can be used to build the better clusters for document. But, there is less research discusses it. Therefore, in this research, the term weighting calculation uses Luhn's Idea to select the terms by defining upper and lower cut-off, and then extracts the feature of terms using gibbs sampling latent dirichlet allocation combined with term frequency and fuzzy Sugeno method.The feature values used to be the distance between documents, and clustered with single, complete and average link algorithm. The evaluations show the feature extraction with and without lower cut-off have less difference. But, the topic determination of each term based on term frequency and fuzzy Sugeno method is better than Tsukamoto method in finding more relevant documents. The used of lower cut-off and fuzzy Sugeno gibbs latent dirichlet allocation for complete agglomerative hierarchical clustering have consistent metric values. This clustering method suggested as a better method in clustering documents that is more relevant to its gold standard.

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Agglomerative Hierarchical Clustering for Information Retrieval Using Latent Semantic Index

Cited by 10 publications

References 21 publications

Text-based multi-dimensional medical images retrieval according to the features-usage correlation

Text-based multi-dimensional medical images retrieval according to the features-usage correlation

Advising Projects to Students using Data Mining and Natural Language Processing

Complete agglomerative hierarchy document’s clustering based on fuzzy luhn’s gibbs latent dirichlet allocation

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