A novel approach on Particle Agent Swarm Optimization (PASO) in semantic mining for web page recommender system of multimedia data: a health care perspective

Manikandan, R.; Saravanan, V.

doi:10.1007/s11042-018-7141-8

Cited by 24 publications

(5 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Web log mining technology can also be applied to security analysis, identifying abnormal logins and attack behaviors by analyzing log les and taking timely measures to protect website security [12]. In addition, the website's performance can be optimized through log mining technology, such as analyzing log les, identifying bottlenecks, adjusting website con guration in time, and improving access speed and responsiveness [13].…”

Section: Application Of Web Log Mining Technology In Data Informatiza...mentioning

confidence: 99%

Web Log Mining Techniques to Optimize Apriori Association Rule Algorithm in Sports Data Information Management

Li,

Liu,

Chen

et al. 2023

Preprint

View full text Add to dashboard Cite

This paper combines the Apriori association rule algorithm and Web application development technology to optimize and upgrade the management system to optimize the current college sports data information management system. On the one hand, the novel log mining technology in web application development technology is introduced. This technology has an excellent performance in improving system performance and understanding user behavior to discuss students’ access habits and content through processing sports data. On the other hand, combined with log mining technology to optimize the Apriori algorithm, the association between sports data information is found through the optimization algorithm. The retrieval accuracy and time are improved, which is convenient for the webmaster to grasp the details of the system. Finally, experiments are used to verify the reliability and effectiveness of the optimized system. The experimental results show that before the algorithm optimization, with the increase in the amount of information, the running time of the Apriori algorithm almost shows a multiplication trend. However, the optimized algorithm has improved its execution efficiency by at least 10–15%, which can verify that the optimized algorithm also exhibits good performance when the amount of information is enormous. Compared with traditional management systems, the optimized system has dramatically improved information retrieval time and accuracy, with an average retrieval accuracy of 98.3% and a retrieval time improvement of 23%. This is because adding the association algorithm improves the correlation between the information. It improves the retrieval accuracy of the system and shortens the retrieval time. Therefore, the technology and algorithm studied here have specific application value in the sports information management system and provide a methodological reference for the information management of other subjects.

show abstract

Section: Application Of Web Log Mining Technology In Data Informatiza...mentioning

confidence: 99%

Web Log Mining Techniques to Optimize Apriori Association Rule Algorithm in Sports Data Information Management

Li,

Liu,

Chen

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…For the WPR system of multimedia data, a particle agent swarm optimization (PASO) was developed in semantic mining by Reference 35. In this research, WPR was related to the healthcare system.…”

Section: Related Workmentioning

confidence: 99%

Deep auto‐encoder based clustering algorithm for graph‐based web page recommendation system

Alagappan

Victor

2022

Concurrency and Computation

View full text Add to dashboard Cite

Web-page recommendation system (RS) plays a major role in smart web systems. The core technology is the RS, which helps users find their interests or discover item preferences. With the rapid proliferation of information technology, the present era witnessed a remarkable growth in the collection and generation of web data. Every day, it is difficult to project the appropriate information to the user and adds severe complexity to the decision making procedure. Moreover, the existing RS techniques have limitations like huge computation time, high computational cost, over specialization, and sparsity problems. This work presents an effective graph-based web page recommendation model to overcome these difficulties. Initially, the input data is preprocessed utilizing Natural Language Tool Kit executed in Python. The snapshot-based dynamic graph model obtains the user-web page relationship. Weight calculation of user-item and the user-user edge is also processed with this graph model. The user-user edge similarity is used to obtain the user preference vector. Then, unsupervised-deep auto encoder based density-based clustering algorithm is proposed to cluster the user preference vector, which groups the visited web page details into k clusters. The web pages are recommended based on the highest score calculated by the page ranking method. Finally, the topmost visited web pages are recommended to web users based on the ranking score. "All the news", MSNBC, and Weblog datasets are taken for evaluation. The proposed method is evaluated with respect to standard measures like F1 measure, accuracy, recall, precision, and root mean square error. Compared with other existing methods, the proposed technique achieved the highest accuracy, 97.99% for All the news datasets, 93.03% for the MSNBC database, and 90.07% for the Weblog database. K E Y W O R D S clustering, density based clustering, dynamic graph-based model, ranking, unsupervised-deep auto encoder, user preference vector 1 INTRODUCTION Recently, recommendation systems (RSs) are largely utilized in several web related applications. The RS is one of the software platforms that renders different kinds of ideas to the user depending on their requirement. RS is also called an information filtering mechanism, which seems like what type of music to listen to, what type of book to read, what type of things to buy and so forth. 1 In the internet, the overloading of information is

show abstract

“…Later, researchers proposed the most famous Apriori algorithm in association rule mining and its variants AprioriTid and AprioriHybrid algorithms to discover frequent item sets [12]. Since then, many scholars have proposed algorithms for discovering frequent item sets of association rules, but most of the algorithms are variants or improvements of the Apriori algorithm [13,14]. Since the Apriori algorithm is a multipass search algorithm, for a large data collection, the external memory must be read once for each search, and the I/O overhead is very high [15].…”

Section: Related Workmentioning

confidence: 99%

Analysis of Rural Tourism Demand Characteristics and Experience Differences Based on Association Rule Mining

Jiang

2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

Whether the characteristics of rural tourism changes or not provides the scale and basis for judging whether the rural tourism landscape has changed, but it cannot provide a judgment on the impact of rural tourism landscape changes. The impact is relative to the rural tourism landscape goal. The determination of rural tourism landscape objectives provides a baseline for judging the direction and impact of rural tourism characteristics and provides a prerequisite for rural tourism landscape actions. The determination of the quality target of the rural tourism landscape is mainly determined by the internal process and external demand of the rural tourism landscape. Through in-depth research on the frequent pattern growth algorithm FP-Growth, the algorithm can find frequent item sets by not generating candidate item sets. The core of the algorithm is the frequent pattern tree FP-tree, which can efficiently compress the transaction database. Based on the advantages of FP-tree, this paper improves a FP_Apriori algorithm based on frequent pattern trees. This algorithm projects the entire transaction database onto the FP-tree, avoiding a lot of I/O overhead. At the same time, I propose a more directional and targeted search strategy for FP-tree, which reduces the running time of the algorithm and uses the principle of the Mapping_Apriori algorithm to prethin the frequent item sets. This article uses the text analysis method of network data to excavate the characteristics and internal structure of rural tourism demand. The rural tourism market has a wide range of needs and multiple levels, and traditional research methods such as questionnaires have limited sample size and sample structure. With the help of network data, text mining, and other statistical analysis methods, in-depth empirical research on the characteristics and spatial structure of rural tourism in a certain region can cover more research groups. The research confirms that the results of using text analysis and questionnaire analysis on the perception of destination image are relatively consistent. Therefore, the network text analysis method is an effective tool to study the demand structure of the rural tourism market.

show abstract

A novel approach on Particle Agent Swarm Optimization (PASO) in semantic mining for web page recommender system of multimedia data: a health care perspective

Cited by 24 publications

References 25 publications

Web Log Mining Techniques to Optimize Apriori Association Rule Algorithm in Sports Data Information Management

Web Log Mining Techniques to Optimize Apriori Association Rule Algorithm in Sports Data Information Management

Deep auto‐encoder based clustering algorithm for graph‐based web page recommendation system

Analysis of Rural Tourism Demand Characteristics and Experience Differences Based on Association Rule Mining

Contact Info

Product

Resources

About