Data mining and text mining — A survey

Suresh, R; Harshni, S. R.

doi:10.1109/iccpeic.2017.8290404

Cited by 13 publications

(3 citation statements)

References 1 publication

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“…Text mining is similar to data mining in certain ways. In data mining, methods are developed to deal with structured data obtained from databases (Suresh & Harshni, 2017). Text mining, on the other hand, can be used to analyze semi-structured or unstructured data sets like full-text documents, emails, HTML files, etc.…”

Section: Text Mining Versus Data Miningmentioning

confidence: 99%

Correlation Evaluation Scale Through Text Mining Algorithms and Implementation on the Kurdish Language - A Review

2023

QZJ

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In recent times, because of the many articles that are found on the web, text extraction has become an interesting area of research. Text mining is a technique that can be used to extract useful information or knowledge from text documents that are not typically in an unstructured form. There are some studies conducted to use different techniques of text production for unstructured data sets. This study will provide an overview of the different methods and algorithms that are related to Text mining and also some studies on the mining of Kurdish web documentation. In addition, a collection of research problems and research methodologies will assist scholars in tracking their future research.

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Section: Text Mining Versus Data Miningmentioning

confidence: 99%

Correlation Evaluation Scale Through Text Mining Algorithms and Implementation on the Kurdish Language - A Review

2023

QZJ

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“…This paper investigates the influence of atmospheric parameters on signal strength in MW frequency band using a data mining technique for a large quantity of data. However, although many researchers exist on data mining to process big data [7,10,11], the assessment of weather parameters changes and its effect on signal strength is normally not performed on data mining to process the data. The researchers showed that the change of atmospheric parameters is effective at the received signal VHF/UHF [9] whereas the weather parameters changes effect on the AM band requires more research.…”

Section: Literature Reviewmentioning

confidence: 99%

Assessing the weight of weather parameters on MW signal strength by descriptive data mining

2020

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In this paper, weather parameters and electric field strength are measured in MW frequency band in Dashte_Ghazvin station. The experimental results were analyzed statistically as well as data mining techniques. Finally, the effects of weather parameters on signal strength were analyzed. In the far-field situations, the electric or magnetic field strength individually can be the representative of the electromagnetic wave radiation. In this paper, the electromagnetic radiation was assessed using measured electric field in terms of Volt per meter that changed from 8.50 to 3.32 V/m. The large amount of data provided the possibility of using data mining technique to find the exact relationship between the weather parameters and signal strength as well as the amount of their effects on signal strength. After the acquisition of data during 1 year, preprocessing of data was performed and weights were obtained through correlation. During a period of 1 year, 9,000,000 data was gathered in 2018 from January to December. The data includes the 5 weather parameters and signal strength. The results showed relationships between atmospheric parameters and the electric field strength. Additionally, the correlation analysis used to assess the relationship between each parameter and signal strength proved that the air humidity and air pressure have an inverted linear relationship with the signal level whereas the other variables have a direct linear relationship. The results are useful to design and develop the station before any construction.

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“…From this graph, it can be easily seen the trend in this research field. Mining data uncover new facts and relationships using useful patterns and techniques in order to give a solution for handling big data [1]. Data mining techniques are implemented to find useful patterns in large database such as MySQL and Oracle.…”

Section: Introductionmentioning

confidence: 99%

WEIDJ: Development of a new algorithm for semi-structured web data extraction

Sabri¹,

Man²

2021

TELKOMNIKA

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In the era of industrial digitalization, people are increasingly investing in solutions that allow their process for data collection, data analysis and performance improvement. In this paper, advancing web scale knowledge extraction and alignment by integrating few sources by exploring different methods of aggregation and attention is considered in order focusing on image information. The main aim of data extraction with regards to semistructured data is to retrieve beneficial information from the web. The data from web also known as deep web is retrievable but it requires request through form submission because it cannot be performed by any search engines. As the HTML documents start to grow larger, it has been found that the process of data extraction has been plagued with lengthy processing time. In this research work, we propose an improved model namely wrapper extraction of image using document object model (DOM) and JavaScript object notation data (JSON) (WEIDJ) in response to the promising results of mining in a higher volume of image from a various type of format. To observe the efficiency of WEIDJ, we compare the performance of data extraction by different level of page extraction with VIBS, MDR, DEPTA and VIDE. It has yielded the best results in Precision with 100, Recall with 97.93103 and F-measure with 98.9547.

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Data mining and text mining — A survey

Cited by 13 publications

References 1 publication

Correlation Evaluation Scale Through Text Mining Algorithms and Implementation on the Kurdish Language - A Review

Correlation Evaluation Scale Through Text Mining Algorithms and Implementation on the Kurdish Language - A Review

Assessing the weight of weather parameters on MW signal strength by descriptive data mining

WEIDJ: Development of a new algorithm for semi-structured web data extraction

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