Text Document Clustering Using K-means Algorithm with Dimension Reduction Techniques

Kumbhar, Rutuja; Mhamane, Saniya; Patil, Harshada; Patil, Sukruta; Kale, Shubhangi

doi:10.1109/icces48766.2020.9137928

Cited by 8 publications

(4 citation statements)

References 7 publications

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“…This work uses TF-IDF, SVM, NMF, and k-means clustering techniques to categorize data. Results are compared using 20 newsgroup datasets, demonstrating the effectiveness of these techniques [9]. Document clustering is a classification process that operates without the need for supervision, wherein documents are grouped together into separate groups based on their similarities and differences.…”

Section: Literature Reviewmentioning

confidence: 99%

Text Classification of National Anthem using Agglomerative Hierarchical Clustering

Rai,

Bista,

Prasun

et al. 2024

IJRP

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Text clustering allows users to categorize different documents based on their similarities. Over the course of several years, this research topic has attracted significant attention from scholars, resulting in the emergence of many approaches and procedures. Nevertheless, the study primarily focuses on English and other languages that have ample resources. This paper presents a comprehensive assessment of clustering methods in the context of national anthems across 190 countries worldwide. The task of conceptually categorizing Anthem is difficult because of its restricted duration. The present study involved the extraction of various features from the anthem, such as stop-words, stemming, corpus tokenization, noise removal, and TF-IDF features. The Agglomerative Hierarchical Clustering technique is utilized for the clustering process.The results indicate that the utilization of a clustering technique in combination with an Agglomerative Hierarchical Clustering algorithm, which incorporates TF-IDF properties, is highly beneficial.

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Section: Literature Reviewmentioning

confidence: 99%

Text Classification of National Anthem using Agglomerative Hierarchical Clustering

Rai,

Bista,

Prasun

et al. 2024

IJRP

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“…In the previous works presented for opinion texts clustering using dimension reduction, mostly linear dimension reduction methods including principal component analysis (PCA), singular value decomposition (SVD), nonnegative matrix factorization (NMF), and linear discriminant analysis (LDA) have been used, and also in some cases, feature selection method has been applied. In [25][26][27][28][29][30], first, dimensions of the opinion texts were reduced by linear dimension reduction methods, and then, clustering was performed. In [25], the TF-IDF model was used to represent the text for clustering; then, dimensions were reduced using the SVD and NMF methods.…”

Section: Opinion Texts Clustering By Dimension Reductionmentioning

confidence: 99%

“…In [25][26][27][28][29][30], first, dimensions of the opinion texts were reduced by linear dimension reduction methods, and then, clustering was performed. In [25], the TF-IDF model was used to represent the text for clustering; then, dimensions were reduced using the SVD and NMF methods. Finally, clustering was performed on the given dimension-reduced texts by the K-Means algorithm.…”

Section: Opinion Texts Clustering By Dimension Reductionmentioning

confidence: 99%

Opinion Texts Clustering Using Manifold Learning Based on Sentiment and Semantics Analysis

Gudakahriz

Moghadam

Mahmoudi

2021

Scientific Programming

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Nowadays, opinion texts are quickly published on websites and social networks by various users in the form of short texts and also in high volumes and various fields. Because these texts reflect the opinions of many users, their processing and analysis, such as clustering, can be very useful in a variety of applications including politics, industry, commerce, and economics. High dimensions of the text representation decrease efficiency of clustering, and an effective solution for this challenge is reducing dimensions of texts. Manifold learning is a powerful tool for nonlinear dimension reduction of high-dimensional data. Therefore, in this paper, for increasing efficiency of opinion texts clustering, by manifold learning, dimensions of the represented opinion texts are reduced based on sentiment and semantics, and their intrinsic dimensions are extracted. Then, the clustering algorithm is applied to dimension-reduced opinion texts. The proposed approach helps us to cluster opinion texts with simultaneous consideration of sentiment and semantics, which has received very little attention in the previous works. This type of clustering helps users of opinion texts to obtain more useful information from texts and also provides more accurate summaries in applications, such as the summarization of opinion texts. Experimental results on three datasets show better performance of the proposed approach on opinion texts in terms of important measures for evaluating clustering efficiency. An improvement of about 9% is observed in terms of accuracy on the third dataset and clustering based on sentiment and semantics.

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“…This demonstrates that the proposed strategy improves the grouping of English texts in text documents. Large data dimensions are a barrier to extracting relevant information, according to RutujaKumbhar and SnehalMhamane [4], hence the dimensions of data matrices are reduced using the dimensionality reduction (DR) technique. TF-IDF, SVD, kmeans clustering and NMF are used to further partition data into clusters using the k-means clustering algorithm.…”

Section: Related Workmentioning

confidence: 99%

Text Clustering using K-MEAN

2021

IJATCSE

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Document clustering allows the user to add similar documents to a group. For many years, it has been a fascinating research topic, developed various methods and techniques. However, the study focuses mostly on English and high-resource languages. About Pakistan national anthems, this research gives an experimental estimation of clustering techniques. Because of its short length, thematically clustering Anthem is a difficult task. This paper extracted various characteristics, including stop-words, stemming, corpus tokenization, noise removal, and TF-IDF features from the anthem, and the clustering was conducted using the K-Means algorithm. The results show that a clustering strategy paired with a K-mean clustering algorithm with TF-IDF features has already been used. The dataset is available on GitHub (https://www.kaggle.com/lucasturtle/national-anthems-of- the-world )

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Text Document Clustering Using K-means Algorithm with Dimension Reduction Techniques

Cited by 8 publications

References 7 publications

Text Classification of National Anthem using Agglomerative Hierarchical Clustering

Text Classification of National Anthem using Agglomerative Hierarchical Clustering

Opinion Texts Clustering Using Manifold Learning Based on Sentiment and Semantics Analysis

Text Clustering using K-MEAN

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