A Method of K-Means Clustering Based on TF-IDF for Software Requirements Documents Written in Chinese Language

Zhu, Jing; Huang, Song; Shi, Yue; Wu, Kaishun; WANG, Yanqiu

doi:10.1587/transinf.2021edp7144

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…The experimental analysis above shows that the extraction of TF-IDF variables results in a multi-marketplace product categorization that is close to the optimal value of the cluster if the grouping formed is four clusters. This is to research conducted [22] that variable extraction is very influential in clustering models, and TF-IDF is a variable extraction method that can provide significant results [42]. Meanwhile, the application of PCA also has a significant influence on cluster results, namely on TF-IDF with a PCA value of 70, and the use of Python can optimize the data so that the results provided can be better.…”

Section: Discussionmentioning

confidence: 99%

Automatic Categorization of Multi Marketplace FMCGs Products using TF-IDF and PCA Features

Indasari

Tjahyanto

2023

SISFOKOM

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The use of technology in line with the increasing number of internet users has caused a shift in the product sales ecosystem to the realm of electronic commerce (electronic commerce). A total of 73.23 customers made purchase transactions using e-commerce and the most purchased products were products classified as Fast Moving Consumer Goods (FMCGs). The increasingly varied FMCGs data coupled with the increasing number of marketplaces is felt to need to be broken down into specific groups. The process is carried out by analyzing e-commerce product information, especially product names, and descriptions. In this study, we propose an automatic categorization of multiple marketplaces using data from multiple marketplaces. Data text is converted into structured data with a series of preprocessing, and comprehensive experiments are carried out to see the extraction performance of variables including TF-IDF, BOW, and N-Gram. All three methods are used to validate text data sets with K-Means grouping results used with the help of PCA to reduce data dimensions. The results show that the performance of the TF-IDF algorithm with a dimension reduction value of 70 and the use of Python can provide optimal results for the percentage of grouping data.

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Section: Discussionmentioning

confidence: 99%

Automatic Categorization of Multi Marketplace FMCGs Products using TF-IDF and PCA Features

Indasari

Tjahyanto

2023

SISFOKOM

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“…TF-IDF is one of the traditional methods based on statistics [21]. It has been used in many different applications, such as document clustering [22], text classification [23], detection of domain name generation algorithms [24], and comparing research trends [25]. Term frequency or word frequency is a rarer method used in information retrieval systems compared to TF-IDF.…”

Section: Related Workmentioning

confidence: 99%

Automatic Extraction of Indonesian Stopwords

Achsan¹,

Suhartanto²,

Wibowo³

et al. 2023

IJACSA

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The rapid growth of the Indonesian language content on the Internet has drawn researchers' attention. By using natural language processing, they can extract high-value information from such content and documents. However, processing large and numerous documents is very timeconsuming and computationally expensive. Reducing these computational costs requires attribute reduction by removing some common words or stopwords. This research aims to extract stopwords automatically from a large corpus, about seven million words, in the Indonesian language downloaded from the web. The problem is that Indonesian is a low-resource language, making it challenging to develop an automatic stopword extractor. The method used is Term Frequency -Inverse Document Frequency (TF-IDF) and presents a methodology for ranking stopwords using TFs and IDFs, which is applicable to even a small corpus (as low as one document). It is an automatic method that can be applied to many different languages with no prior linguistic knowledge required. There are two novelties or contributions in this method: it can show all words found in all documents, and it has an automatic cut-off technique for selecting the top rank of stopwords candidates in the Indonesian language, overcoming one of the most challenging aspects of stopwords extraction.

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An approach of using social media data to detect the real time spatio-temporal variations of urban waterlogging

Chen,

Hu,

Chen

et al. 2023

Journal of Hydrology

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A Method of K-Means Clustering Based on TF-IDF for Software Requirements Documents Written in Chinese Language

Cited by 3 publications

References 9 publications

Automatic Categorization of Multi Marketplace FMCGs Products using TF-IDF and PCA Features

Automatic Categorization of Multi Marketplace FMCGs Products using TF-IDF and PCA Features

Automatic Extraction of Indonesian Stopwords

An approach of using social media data to detect the real time spatio-temporal variations of urban waterlogging

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