scholarly journals Closed sequential pattern mining for sitemap generation

2020 ◽  
Author(s):  
Michelangelo Ceci ◽  
Pasqua Fabiana Lanotte
Keyword(s):  
Web Site ◽  
Pattern Mining ◽  
Sequential Pattern Mining ◽  
Design Concept ◽  
Sequential Pattern ◽  
Website Design ◽  
Web Graph ◽  
Textual Content ◽  
Website Content ◽  
The Web

Abstract A sitemap represents an explicit specification of the design concept and knowledge organization of a website and is therefore considered as the website’s basic ontology. It not only presents the main usage flows for users, but also hierarchically organizes concepts of the website. Typically, sitemaps are defined by webmasters in the very early stages of the website design. However, during their life websites significantly change their structure, their content and their possible navigation paths. Even if this is not the case, webmasters can fail to either define sitemaps that reflect the actual website content or, vice versa, to define the actual organization of pages and links which do not reflect the intended organization of the content coded in the sitemaps. In this paper we propose an approach which automatically generates sitemaps. Contrary to other approaches proposed in the literature, which mainly generate sitemaps from the textual content of the pages, in this work sitemaps are generated by analyzing the Web graph of a website. This allows us to: i) automatically generate a sitemap on the basis of possible navigation paths, ii) compare the generated sitemaps with either the sitemap provided by the Web designer or with the intended sitemap of the website and, consequently, iii) plan possible website re-organization. The solution we propose is based on closed frequent sequence extraction and only concentrates on hyperlinks organized in “Web lists”, which are logical lists embedded in the pages. These “Web lists” are typically used for supporting users in Web site navigation and they include menus, navbars and content tables. Experiments performed on three real datasets show that the extracted sitemaps are much more similar to those defined by website curators than those obtained by competitor algorithms.

scholarly journals A Review on Design and Development Of Sequential Patterns Algorithms In Web Usage Mining

2021 ◽  
Vol 12 (2) ◽  
pp. 1634-1639
Author(s):  
V Aruna, Et. al.
Keyword(s):  
Web Mining ◽  
Pattern Mining ◽  
Pattern Discovery ◽  
Relevant Information ◽  
Sequential Pattern Mining ◽  
Web Usage Mining ◽  
Sequential Pattern ◽  
Web Usage ◽  
Hidden Data ◽  
The Web

In the recent years with the advancement in technology, a  lot of information is available in different formats and extracting the  knowledge from that data has become a very difficult task. Due to the vast amount of information available on the web, users are finding it difficult to extract relevant information or create new knowledge using information available on the web. To solve this problem  Web mining techniques are used to discover the interesting patterns from the hidden data .Web Usage Mining (WUM), which is one  of the subset of  Web Mining helps in extracting the hidden knowledge present in the Web log  files , in recognizing various interests of web users and also in  discovering customer behaviours. Web Usage mining  includes different phases of data mining techniques called Data Pre-processing, Pattern Discovery & Pattern Analysis. This paper presents an updated focused survey on various sequential pattern mining  algorithms  like  apriori-based algorithm , Breadth First Search-based strategy, Depth First Search strategy,  sequential closed-pattern algorithm and Incremental pattern mining algorithm which are used in Pattern Discovery Phase of WUM. At last , a comparison  is done based on the important key features present in these algorithms. This study gives us better understanding of the approaches of sequential pattern mining.

scholarly journals A Learning Analytics Framework to Analyze Corporal Postures in Students Presentations

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1525
Author(s):  
Felipe Vieira ◽  
Cristian Cechinel ◽  
Vinicius Ramos ◽  
Fabián Riquelme ◽  
Rene Noel ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Data Collection ◽  
Pattern Mining ◽  
New Technologies ◽  
Personal Information ◽  
Sequential Pattern Mining ◽  
Complex Problem ◽  
Sequential Pattern ◽  
Background Information ◽  
Professional Relationships

Communicating in social and public environments are considered professional skills that can strongly influence career development. Therefore, it is important to proper train and evaluate students in this kind of abilities so that they can better interact in their professional relationships, during the resolution of problems, negotiations and conflict management. This is a complex problem as it involves corporal analysis and the assessment of aspects that until recently were almost impossible to quantitatively measure. Nowadays, a number of new technologies and sensors have being developed for the capture of different kinds of contextual and personal information, but these technologies were not yet fully integrated inside learning settings. In this context, this paper presents a framework to facilitate the analysis and detection of patterns of students in oral presentations. Four steps are proposed for the given framework: Data collection, Statistical Analysis, Clustering, and Sequential Pattern Mining. Data Collection step is responsible for the collection of students interactions during presentations and the arrangement of data for further analysis. Statistical Analysis provides a general understanding of the data collected by showing the differences and similarities of the presentations along the semester. The Clustering stage segments students into groups according to well-defined attributes helping to observe different corporal patterns of the students. Finally, Sequential Pattern Mining step complements the previous stages allowing the identification of sequential patterns of postures in the different groups. The framework was tested in a case study with data collected from 222 freshman students of Computer Engineering (CE) course at three different times during two different years. The analysis made it possible to segment the presenters into three distinct groups according to their corporal postures. The statistical analysis helped to assess how the postures of the students evolved throughout each year. The sequential pattern mining provided a complementary perspective for data evaluation and helped to observe the most frequent postural sequences of the students. Results show the framework could be used as a guidance to provide students automated feedback throughout their presentations and can serve as background information for future comparisons of students presentations from different undergraduate courses.

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