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International Conference of Information Science and Management Engineering (ISME)

作者： Zhu, Hongming He, Yue Sichuan Univ Sch Business Adm Chengdu Peoples R China

ISBN: (纸本)9781845648299;9781845648282

This paper uses attribute reduction algorithm of rough set to remove redundant attributes, aiming to obtain smaller decision data sets, and then apriori algorithm is used to extract tourist characteristics. The conclusions obtained are, male tourists who are 26-35 years old and have 5000-10000 yuan of monthly income are more satisfied with Jiuzhaigou, middle-aged tourists who have 1001-3000 yuan of monthly income and travel 1-3 times yearly are more willing to revisit Jiuzhaigou, tourists who have high education level and have been to Jiuzhaigou for 2-3 times are more willing to introduce Jiuzhaigou to their friends. The conclusions can help promote tourist satisfaction, increase the number of regulars, and increase the targeted marketing effect.

关键词： Rough set theory apriori algorithm tour Jiuzhaigou

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Mining Association Rules in Commuter Feedback Comments from Facebook of Swiss National Railways (SBB) Using apriori algorithm 3rd

Mining Association Rules in Commuter Feedback Comments from ...

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3rd EAI International Conference on Data and Information in Online Environments (DIONE)

作者： Blatter, Patrick Einsele, Farshideh Distance Univ Appl Sci Zollstr 17 CH-8005 Zurich Switzerland Bern Univ Appl Sci Bruckenstr 63 CH-3002 Bern Switzerland

ISBN: (纸本)9783031223235;9783031223242

Nowadays, all kinds of service-based organizations open online feedback possibilities for customers to share their opinion. Swiss National Railways (SBB) uses Facebook to collect commuters' feedback and opinions. These customer feedbacks are highly valuable to make public transportation option more robust and gain trust of the customer. The objective of this study was to find interesting association rules about SBB's commuters pain points. We extracted the publicly available FB visitor comments and applied manual text mining by building categories and subcategories on the extracted data. We then applied apriori algorithm and built multiple frequent item sets satisfying the minsup criteria. Interesting association rules were found. These rules have shown that late trains during rush hours, deleted but not replaced connections on the timetable due to SBB's timetable optimization, inflexibility of fines due to unsuccessful ticket purchase, led to highly customer discontent. Additionally, a considerable amount of dis-satisfaction was related to the policy of SBB during the initial lockdown of the Covid-19 pandemic. Commuters were often complaining about lack of efficient and effective measurements from SBB when other passengers were not following Covid-19 rules like public distancing and were not wearing protective masks. Such rules are extremely useful for SBB to better adjust its service and to be better prepared by future pandemics.

关键词： Opinion mining Data mining Association analysis apriori algorithm Online customer feedback mining Text mining

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Inferring implications in semantic maps via the apriori algorithm

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LINGUA 2020年 239卷

作者： Zeng, Ni Xiao, Hong Wuhan Univ Coll Chinese Language & Literature 299 Bayi Rd Wuhan 430072 Peoples R China

Semantic maps are powerful tools for analyzing cross-language variations with implications between semantic functions to construct the relevant conceptual space. However, as existing semantic maps cannot illustrate the imbalance of implications between functions, a further discussion of inferring implications is highly demanded. The problem of inferring implications and the imbalance of implications between functions above is similar to the well-known problem of generating all significant association rules between items purchased by customers, and the apriori algorithm offers an effective solution to relieve such issue. Here, alternative schematic diagrams based on the apriori algorithm are employed to supplement semantic maps, which is justified by reproducing the same results as Cysouw obtained on person marking using his datasets. Furthermore, in our study, an implication in number from singular to plural is observed in person marking. We have solved the issue of imbalance and obtained credible implication rules between primitives in groups. We can mine practicable directed implication rules and reveal rules hard to notice before, such as active primitives between groups. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Implications Semantic maps Typology apriori algorithm

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Improving Search Results Based on Users' Browsing Behavior Using apriori algorithm 50th

Improving Search Results Based on Users' Browsing Behavior U...

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50th Annual Convention of the Computer-Society-of-India (CSI) on Digital Life

作者： Deepika Juneja, Shilpa Dixit, Ashutosh YMCA Univ Sci & Technol Dept Comp Engn Faridabad India

ISBN: (纸本)9789811088483;9789811088476

World Wide Web (WWW) is decentralized, dynamic, and diverse. It is growing exponentially in size. To improve search results, various ranking methods are being used. Due to vast information on the Web, there is a need to build an intelligent technique that automatically evaluates Web pages that are of user interest. In this paper, interest of a user in a particular Web page can be estimated by his browsing behavior without incurring additional time and effort by the user. It can also adapt to changes in user's interests over time. A page ranking mechanism is being proposed which takes user's actions into account. For this, a Web browser has been developed to store user's behavior. apriori algorithm is applied on the data collected by Web browser which results in most frequent actions out of all actions. A calculated confidence value has been used to calculate weight of the Web page. Higher the weight, higher the rank.

关键词： World wide web apriori algorithm Browsing behavior Actions Web browser PageRank

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Web Usage Pattern Detection Using Cohesive Markov Model With apriori algorithm

Web Usage Pattern Detection Using Cohesive Markov Model With...

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2022 International Conference on Automatic Control and Intelligent Systems

作者： Luckose, Vinukumar Chembath, Jothish Ponnusamy, Joe Arun Raja Sharma, Sakshi Kaur, Pritpal Smiley, Sajitha SEGi Univ Ctr Adv Elect & Elect Syst FOEBEIT Petaling Jaya Malaysia Koneru Lakshmaiah Educ Fdn Dept CSE Vijayawada India Chandigarh Univ Univ Inst Comp Mohali India Manonmaniam Sundaranar Univ Tirunelveli India

ISBN: (纸本)9781665495813

Web server maintains the essential user log files, recording every request to it. Web log is a record of events which includes all the user details from the time the web visitor initiated the session to the end of the session. The web usage pattern discovery to identify different states of the user access behavior on web. The design of web recommender system using a context-aware Cohesive Markov Model and apriori clustering is proposed. The prediction rate of proposed algorithm is higher than conventional Markov model.

关键词： Web Usage mining Weblogs Pattern Discovery Cohesive Markov Model apriori algorithm

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Diversifying Recommendations of Collaborative Filtering using apriori algorithm for Movies 26

Diversifying Recommendations of Collaborative Filtering usin...

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26th International Conference on Computer and Information Technology, ICCIT 2023

作者： Hasan, S.M. Nahid Rifat Hasan, Md Islam, Anika Tasnim Yasmeen, Fauzia Fareast International University Department of Computer Science & Engineering Dhaka Bangladesh

ISBN: (纸本)9798350359015

Many algorithms are popular to generate personalized recommendations based on user preferences for movies. One such algorithm that is very famous nowadays is Collaborative Filtering. However, a common issue with collaborative filtering is the tendency to recommend popular movies, which often leads to a lack of diversity in the recommended items. To solve this problem, we propose a method for collaborative filtering that uses the apriori algorithm to diversify movie selections. apriori algorithm is a widely used association rule mining tool. Integrating it with the collaborative filtering framework we can find common itemsets that capture co-occurrence patterns among movies. The algorithm introduces diversity constraints based on the identified frequent itemsets to enhance recommendation diversity. These constraints ensure recommendations reducing the bias towards popular movies. Finally, the proposed method was evaluated on real-world movie datasets. The results demonstrate that incorporating the apriori algorithm into collaborative filtering significantly enhances recommendation diversity, giving users more diverse and personalized movie recommendations. © 2023 IEEE.

关键词： apriori algorithm Collaborative filtering Pattern Mining Recommendation System

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A Novel prediction of sales and purchase forecasting for festival season of Hypermarkets with customer dataset using apriori algorithm instead of FP-Growth algorithm to improve the accuracy 1

A Novel prediction of sales and purchase forecasting for fes...

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1st International Conference on Technologies for Smart Green Connected Society 2021, ICTSGS 2021

作者： Karthik, Somu Velu, C.M. Saveetha University Chennai India

ISBN: (纸本)9781607685395

To predict the novel and to Forecast sales for festival season Hypermarkets. A total of 484 samples were collected from market datasets available in kaggle. For this two algorithms were used, one is the FP-Growth algorithm and another is apriori algorithm, both the algorithms were executed and compared for accuracy. aprioriachieved accuracy, precision, sensitivity and specificity of 73 %,75%,78%and 80% respectively compared to 71%,73%,76%,75% and 78% by FP-Growth algorithm, 87.4%, 88.2%, 89.2% and 93% respectively compared to 80.1%, 83.39%, 84% and 86.20% by apriori algorithm. The results were obtained with a level of significance (p © The Electrochemical Society

关键词： apriori algorithm Artificial Intelligence FP-Growth algorithm Itemsets Machine Learning Novel prediction Recursive Feature Elimination Transaction datasets

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Design and Research of Scientific Research Management System Based on apriori algorithm 3

Design and Research of Scientific Research Management System...

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3rd IEEE International Conference on Electrical Engineering, Big Data and algorithms, EEBDA 2024

作者： Li, Nan Qi, Jinsheng Changchun Automobile Industry Institute Jilin Changchun China

ISBN: (纸本)9798350380989

With the increasing development of technology, research activities and capabilities of universities have become important indicators for measuring their comprehensive strength. The assessment of scientific research achievements has also become one of the indicators for measuring the year-end performance of university teachers. Therefore, this article proposes using ASP Design a four layer network architecture research management system using NET as the development platform, C# development language, and B/S structure programming model. The system uses an improved association rule apriori algorithm to perform association analysis on scientific research assessment information, and quantifies the analysis results. The results indicate that the system can provide reference for the design of dynamic indicators for scientific research assessment of teachers, and provide reference conditions for the school's scientific research incentive policies. © 2024 IEEE.

关键词： apriori algorithm association analysis data mining four layer network architecture research management system

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CCHP Intelligent Optimization System of Enterprise Incentive Policy Based on Improved apriori algorithm 3

CCHP Intelligent Optimization System of Enterprise Incentive...

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3rd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Hu, Liqin Liu, Hong Chengdu Neusoft University Department of Information Management Sichuan Chengdu China

ISBN: (纸本)9798350317022

With the passage of time, the new generation of employees enters the workplace and gradually becomes the backbone of the enterprise. They are passionate and innovative, but their strong self-awareness and tendency to leave their jobs have brought a lot of management problems to the company. A sound incentive mechanism is the best way to reduce employee turnover and improve employee loyalty, which is of great significance for stabilizing the talent team of the enterprise. Company A is an e-commerce company in the growth stage, and the employee group is mainly young people, which also has the problem of high employee turnover rate, which hinders the rapid development of the company. Based on this, this paper constructs a research model with the incentive mechanism of company A as the independent variable and the employee loyalty of company A as the dependent variable based on the relevant theories. The improved apriori algorithm was used to analyze the collected data by taking the employees of company A as the survey object, and finally the conclusion that the incentive mechanism of company A had a significant positive impact on the loyalty of its employees was finally obtained, which provided basic support for the CCHP intelligent optimization system of enterprise incentive policy in the next step. © 2023 IEEE.

关键词： apriori algorithm CCHP Intelligent Optimization System Employee Loyalty Incentives Relationship Studies

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Simulation of Big Data AR Intelligent Tourism System Based on Improved apriori algorithm 3

Simulation of Big Data AR Intelligent Tourism System Based o...

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3rd International Conference on Artificial Intelligence and Autonomous Robot Systems, AIARS 2024

作者： Li, Lin Wang, Hongli Jilin Agricultural Science and Technology University Jilin China

ISBN: (纸本)9798350376173

This paper proposes a simulation method of big data AR intelligent tourism system based on improved apriori algorithm, aiming at improving the intelligent level and user experience of the tourism system. Firstly, the application background and significance of apriori algorithm in big data processing are introduced, and then the application method and process of improved apriori algorithm in AR intelligent tourism system simulation are expounded in detail. Finally, the effectiveness and feasibility of the improved apriori algorithm are verified by experiments, and compared with other related studies. The experimental results show that the improved apriori algorithm can effectively deal with large amount of data, and improve the simulation efficiency and user experience of AR intelligent tourism system. © 2024 IEEE.

关键词： apriori algorithm AR intelligent tourism system big data simulation user experience

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