A survey paper on techniques and applications of web usage mining

Jain, Subhi; Rawat, Ruchira; Bhandari, Bina

doi:10.1109/icetcct.2017.8280343

Cited by 5 publications

(3 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The last source of science-related work is used in learning. For research, this methodology has the drawback of providing information not relevant to our subject matter when we are looking for our issue (Phyu and Wai, 2019;Jain et al, 2017).…”

Section: Web Content Miningmentioning

confidence: 99%

Text Document Clustering using Hashing Deep Learning Method

Swidan¹,

Guirguis²,

Abood³

2020

Int. J. Soft Comput.

View full text Add to dashboard Cite

Web mining is the method of analyzing an grouping of behavioral, statistic, way of life, value-based, web and geographic data for the personalization of offers to online shoppers in genuine time. The goal of this study is to build an effective model for the use of hybrid data clustering and classification technology to evaluate online news data. Assess the best way to use site news information algorithms and assess the reliability of the online news databases use tools and techniques for data mining. A well-known platform to share information among online users is a web-based application. However, nowadays, it is the most challenge to handle gigantic data or enormous information such as web news or web-based promoting by users. On the other side, web applications are the most readily available medium for consumers to access up-to-date information. Such apps also need tremendous space, time and drain the battery power of the mobile devices of the users. One solution to mitigate these challenges is therefore, to extract or extract certain information on the basis of certain characteristics. In contrast, the attributes are the actions or the information collected from different sources by the consumer. This essay attempts to design and implement a web app to extract information on geolocation and space and provides a comparative study on three specific mining techniques.

show abstract

Section: Web Content Miningmentioning

confidence: 99%

Text Document Clustering using Hashing Deep Learning Method

Swidan¹,

Guirguis²,

Abood³

2020

Int. J. Soft Comput.

View full text Add to dashboard Cite

show abstract

“…Most recent classifications in web mining are focused on web usage mining, as described by Jain et al (2017) and Kumar et al (2017). This area was not well covered in earlier classifications, but the existing ones on web usage mining usually only focus on one specific aspect.…”

Section: Existing Web Mining Taxonomiesmentioning

confidence: 99%

“…This area was not well covered in earlier classifications, but the existing ones on web usage mining usually only focus on one specific aspect. For example, Jain et al (2017) focused on the various stages of the web usage mining process, while Kumar et al (2017) classified web usage mining based on the data source.…”

Section: Existing Web Mining Taxonomiesmentioning

confidence: 99%

Internet Web page content block dataset and solutions for its data labelling simplification

Griazev

View full text Add to dashboard Cite

Vadovė prof. dr. Simona RAMANAUSKAITĖ (Vilniaus Gedimino technikos universitetas, Informatikos inžinerija -T 007). Vilniaus Gedimino technikos universiteto Informatikos inžinerijos mokslo krypties disertacijos gynimo taryba: Pirmininkas prof. dr. Dalius MAŽEIKA (Vilniaus Gedimino technikos universitetas, Informatikos inžinerija -T 007). Nariai: dr. Jolita BERNATAVIČIENĖ (Vilniaus universitetas, Informatika -N 009), dr. Robertas DAMAŠEVIČIUS (Kauno technologijos universitetas, Informatikos inžinerija -T 007), prof. dr. Arnas KAČENIAUSKAS (Vilniaus Gedimino technikos universitetas, Informatikos inžinerija -T 007), dr. Kristo KARJUST (Talino technikos universitetas, Estija, Informatikos inžinerija -T 007). Disertacija bus ginama viešame Informatikos inžinerijos mokslo krypties disertacijos gynimo tarybos posėdyje 2024 m. birželio 12 d. 14 val. Vilniaus Gedimino technikos universiteto SRA-I posėdžių salėje.

show abstract