Short Survey Of Data Mining And Web Mining Using Cloud Computing

Phyu, Aye Pwint; Thu, Ei Ei

doi:10.35444/ijana.2021.12509

Cited by 4 publications

(4 citation statements)

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“…Although it is feasible to have a local level in the web's link structure, doing it globally is rather challenging. Therefore, global link analysis algorithms have rather effective anti-spam protections [16].…”

Section: Web Structure Mining (Wsm)mentioning

confidence: 99%

Links Evaluation and Ranking Based on Semantic Metadata Analysis

Abdulmunim,

Naamha

2024

Iraqi Journal of Science

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There is a vast quantity of information contained within the billions of web pages that make up the World Wide Web (WWW). Search engines carry out a variety of activities depending on their own architectures for retrieving the necessary information from the WWW. The search engine typically returns a huge number of pages in response to a user's query when the user submits one. Numerous ranking techniques have been utilized on search results to aid consumers in navigating the result list. The majority of ranking algorithms described in literature are either content-based or link-based, and they do not take user usage patterns into account. The presented study discusses web mining ranking algorithms depending on structures, contents, and usages and suggests a new ranking method to assess the significance of links with the use of semantic metadata analysis, which considers the number of links visited across nearly all regions, time periods, and related topics and queries. Furthermore, the suggested system uses the user's query to find more relevant information. The most valuable pages are thus displayed at the top of the result list based on user browsing behavior, which significantly decreases the search space. Results showed better ranking output based on different criteria, such as the number of links visited yearly, the number of links visited hourly, the number of links visited by region, the number of links visited by related topics, and the number of links visited by related queries.

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Section: Web Structure Mining (Wsm)mentioning

confidence: 99%

Links Evaluation and Ranking Based on Semantic Metadata Analysis

Abdulmunim,

Naamha

2024

Iraqi Journal of Science

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“…Classification Mining is depends on developing in a profile of items that belong to a particular group classified according to their common attributes. The profile can classify any new items are added as well as update the database accordingly [13]. Classified mining allows the developer to build a profile for clients who can able to access particulate web usage mining link page based on the demographic information available.…”

Section: Classification Miningmentioning

confidence: 99%

A Survey on Machine Learning Based Classification Algorithms for Web Usage Mining

Punithavathi

2024

IJRASET

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Machine learning as well as data mining is research areas of computer technology whose quick expansion is due to the advances in data analysis research, growth in the database industry as well as the resulting market requirements for methods that are accomplished of extracting valuable knowledge from large data stores. It is a powerful platform that stores as well as retrieves mass information besides it becomes a time consuming uncomfortable task to search the information due to unstructured as well as heterogeneous nature of data on the web development. Web usage mining is one of the popular techniques of data mining that is utilized to discover besides quotation useful information from web document besides its service. This survey paper, conduct a comprehensive overview of the state of the art methods, clustering based machine learning based algorithms and its advantages and disadvantages of web usage mining.

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“…e traditional Apriori algorithm uses support as the determinant for generating candidate item sets, and some of the generated association rules have meaningless "pseudo" association rules which are easy to cause "illusion" [13].…”

Section: Matrix Clustering Apriori Algorithm Modelmentioning

confidence: 99%

Personalized Hybrid Recommendation for Tourist Users Based on Matrix Cluster Apriori Mining Algorithm

Zhang

2022

Mathematical Problems in Engineering

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With the rapid development of Internet technology and the arrival of the era of big data, the rapid expansion of network information resources has formed massive information. Massive information resources have brought great convenience to people’s lives. However, it becomes more and more difficult to find the content that interests you, which is the phenomenon of “information overload.” In order to solve this problem, a solution based on personalized recommendation technology is proposed. In personalized recommendation technology, collaborative filtering algorithm is the most widely used technology. Clustering technology can effectively divide objects into groups, so that the similarity of attributes between objects in the same group is high, and the similarity of objects in different groups is low. The core step of the filtering recommendation algorithm is to find the similar neighbors of the target user by calculating the similarity. Applying the clustering technology to the recommendation can effectively improve the performance of the recommendation system. Aiming at the real-time problem of collaborative filtering recommendation, this paper introduces a method of firstly clustering users on the user item rating matrix, and finding the nearest neighbors in the clusters with high similarity with the target user, which effectively reduces the query space and improves the recommendation. This paper proposes a method to measure the user’s preference for item attributes, which is used in the above clustering process to improve the recommendation accuracy while retaining the advantage of reducing the query space. Aiming at the problem of poor recommendation accuracy, this paper proposes a fuzzy-improved K-means algorithm to cluster items in the product attribute matrix, and then fuses the similarity of the belongingness of items to clusters in the fuzzy clustering. The similarity calculated on the score matrix shows that this method is better than the traditional hybrid recommendation in accuracy.

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Short Survey Of Data Mining And Web Mining Using Cloud Computing

Cited by 4 publications

References 0 publications

Links Evaluation and Ranking Based on Semantic Metadata Analysis

Links Evaluation and Ranking Based on Semantic Metadata Analysis

A Survey on Machine Learning Based Classification Algorithms for Web Usage Mining

Personalized Hybrid Recommendation for Tourist Users Based on Matrix Cluster Apriori Mining Algorithm

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