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Developing a novel H-apriori algorithm using support-leverage matrix for association rule mining

International Journal of Information Technology (Singapore) 2024年第8期16卷 5395-5405页

作者： Pamnani, Harish Kumar Raja, Linesh Ives, Thom Department of Computer Applications Manipal University Jaipur Rajasthan Jaipur India Echo Global Logistic 600 W Chicago Ave #725 Chicago 60654 IL United States

The importance of e-commerce platforms is playing a vital role in human life. Data mining plays a crucial role in e-commerce platforms, allowing them to extract valuable information from extensive datasets by examining buying tendencies from customers using Association rule mining and Market basket analysis. In this research paper, the author proposed a heuristic H-apriori algorithm built on the support-leverage of an item and the effective weight of a transaction using a single dataset scan. The trial estimation contains five sample transactional datasets with variable sizes, ranging from 20 to 7500 transactions. The performance of H-apriori algorithm is compared with state-of-the-art algorithms, Improved-A priori, Hybrid-apriori, and classic Fp-Growth. Improved-apriori and Hybrid-apriori are relying on queue data structure, while classic Fp-Growth is based on a tree data structure. The results of this study demonstrate that the proposed heuristic H-apriori algorithm outperforms the improved A priori, Hybrid-apriori, and Fp-Growth with accuracy of frequent item set and interesting item set count, taking up to 99%,75%, and 93% less run time for frequent item set and up to 91% for interesting item set, as well as up to 91%,15%, and 13% less memory consumption. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： apriori algorithm Association Rule Mining Data Mining Frequent item mining

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Dynamic Location Area Planning in Cellular Network using apriori algorithm

Dynamic Location Area Planning in Cellular Network using Apr...

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International Conference on Industrial Instrumentation and Control

作者： Prajapati, N. B. Kathiriya, D. R. Gujarat Technol Univ BVM Engn Coll Comp IT Engn Ahmadabad Gujarat India Anand Agr Univ Ctr Comp Anand 388110 Gujarat India

ISBN: (纸本)9781479971657

Every human being has predefined movement in their regular social life. This predefined movement of user is very much useful in cellular network. Using supervise learning methods we can able to find out mobility patterns of mobile users in cellular network, which is useful to divide mobile users in various categories. Based on these categories we can develop algorithms which can help cellular network for reducing location management cost (Location Update cost + Paging Cost). In this paper, apriori algorithm is used for creating dynamic location area for mobile users using mobility log of mobile users. The performance of the method has been verified for dynamic location area creation by considering different mobility logs of mobile users by taking different minimum support in apriori algorithm. The simulation has achieved an average of 80% minimum support can be used to create dynamic location area for mobile users and 60%, 40% and 20% minimum support work as an adaptive search for mobile users.

关键词： Mobile users Dynamic Location Area Cellular Network apriori algorithm Location Management Cost Location Update and Paging

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Research and Design for Intrusion Detection System with Hybrid Detector and apriori algorithm

Research and Design for Intrusion Detection System with Hybr...

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2nd International Conference on E-Business and Information System Security (EBISS)

作者： Zhao, Duanyang Xu, Qingxiang Feng, Zhilin Zhejiang Univ Technol Zhijiang Coll Hangzhou 310024 Zhejiang Peoples R China

ISBN: (纸本)9781424458950

Network and host Intrusion Detection Systems (IDS) have become a standard component in security infrastructures. As the action of intrusion represents variable, complicated, and uncertainty characteristic, they face so many problems to resolve for intrusion detection. Each approach has its strengths and weaknesses. We propose a hybrid IDS, which combines network and host IDS, with anomaly and misuse detection mode, utilizes auditing programs to extract an extensive set of features that describe each network connection or host session, and applies data mining programs to learn rules that accurately capture the behavior of intrusions and normal activities. We use an association rule to track all relevant data dependency rule sets for different access roles using a hierarchical structure. We identify malicious transactions from the transaction logs in the database using the data dependency rule sets. These rule sets are continuously updated and stored in a repository. The optimized algorithm actually improves the performance of IDS. Our approach is shown to reduce data access bottlenecks, and ensures minimal manual intervention for maintaining a secure database.

关键词： intrusion detection hybrid ids data mining analysis engine apriori algorithm

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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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An Extension of the apriori algorithm for Finding Frequent Items 6

An Extension of the Apriori Algorithm for Finding Frequent I...

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6th International Conference on Web Research (ICWR)

作者： Karimtabar, Noorollah Fard, Mohammad Javad Shayegan Tech & Vocat Univ Dept Comp Engn Babol Iran Univ Sci & Culture Dept Comp Engn Tehran Iran

ISBN: (纸本)9781728110516

The main purpose of data mining is to discover hidden and valuable knowledge from data. The apriori algorithm is inefficient due to bulky deals of searching in a dataset. Bearing this in mind, this paper proposes an improved algorithm from apriori using an intelligent method. Proposing an intelligent method in this study is to fulfill two purposes: First, we demonstrated that to create itemsets, instead of adding one item at each step, several items could be added. With this operation, the number of k-itemset steps will decline. Secondly, we have proved that by storing the transaction number of each itemset, there would be a diminishment in the time required for the dataset searches to find the frequent k-itemset in each step. To evaluate the performance, the Intelligent apriori (IAP) algorithm has been compared with the MDC algorithm. The results of this experiment exhibit that since the transaction scans used to obtain the itemset momentously reduced in number, there was a considerable fall in the runtime needed to obtain a frequent itemset by the proposed algorithm. In this study, the time required to generate frequent items had a 46% reduction compared to that of the MDC_apriori algorithm.

关键词： Data Mining apriori algorithm Intelligent Method Frequent Itemset

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Using the apriori algorithm to explore caregivers’ depression by the combination of the patients with dementia and their caregivers

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Risk Management and Healthcare Policy 2021年 14卷 2953-2963页

作者： Chang, Cheng-Chen Wang, Wen-Fu Li, Yi-Ying Chen, Yu-An Chen, Yen-Jen Liao, Yi-Cheng Jhang, Kai-Ming Wu, Hsin-Hung Department of Psychiatry Changhua Christian Hospital Changhua Taiwan School of Medicine Chung Shan Medical University Taichung City Taiwan Department of Neurology Changhua Christian Hospital Changhua Taiwan Department of Holistic Wellness Ming Dao University Changhua Taiwan Department of Business Administration National Changhua University of Education Changhua Taiwan Department of M-Commerce and Multimedia Applications Asia University Taichung City Taiwan Faculty of Education State University of Malang Malang East Java Indonesia

Purpose: The purpose of this study was to identify the caring scenarios that result in severe depression in caregivers caring for dementia patients. Patients and Methods: A cross-sectional study with 1111 dementia patients and their caregivers in Taiwan from October 2015 to January 2020 was conducted. Gender, age, type of dementia, clinical dementia rating, walking ability, mood symptoms, behavioral symptoms, and psychological symptoms were the variables from the dementia patients. Age, relation to the patient, employment, type of primary care, frequency of care, mood symptoms, and the score from the Center for Epidemiologic Studies Depression Scale were the variables from the caregivers. A comprehensive viewpoint of both dementia patients and their caregivers was evaluated by the apriori algorithm to find the attributes resulting in different caregiving depressions. Results: Forty-seven rules were found with 18 rules of mild depressive symptomatology, 17 rules of moderate depressive symptomatology, and 12 rules of severe depressive symptoma-tology. A total of 7 general rules were summarized to be the severe depressive symptomatol-ogy. The results showed that an unemployed or retired caregiver with the mood symptoms such as helplessness, anger, emotional liability, or anxiety who took care of AD patients or AD patients with a moderate severity would have severe depression. Increased care frequencies (≥6 days per week) and multiple mood problems from caregivers result in severe depression. The composition of adult children, patients’ aggression, and caregivers’ helplessness as well as the combinations of male patients aged 75–84 years with the caregiver’s mood of helplessness or nervousness and hopelessness were highly associated with severe depression. Conclusion: For those caring for AD patients, severe depression was associated with the combination of different parameters to constitute each of these seven scenarios. Unlike previous studies which often evaluated on

关键词： Alzheimer’s disease apriori algorithm Behavioral and psychological symptoms of dementia Caregiver Caregiver’s mood Caring scenario Center for Epidemiologic Studies Depression Scale Depressive symptomatology Patient with dementia

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Correlation Analysis of Sensor Fault Based on Fuzzy Petri Net and apriori algorithm 6th

Correlation Analysis of Sensor Fault Based on Fuzzy Petri Ne...

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Conference of The-Efficiency-and-Performance-Engineering-Network (TEPEN) / 6th International conference on Maintenance Engineering (IncoME)

作者： Xu, Chuannuo Zhao, Shenglei Hao, Haitao Zhang, Yandong Li, Jiming Cheng, Xuezhen Shandong Univ Sci & Technol Coll Elect Engn & Automat Qingdao 266590 Peoples R China Anju Coal Mine Shandong Fining Min Grp Co Ltd Jining 272000 Peoples R China

ISBN: (纸本)9783030990756;9783030990749

Due to the complex internal structure of the sensor, the corresponding fault causes are also diverse. Once a fault occurs, the cause of the fault is difficult to determine. This paper proposes a sensor fault correlation analysis method combining fuzzy Petri net (FPN) and apriori algorithm. First, obtain the typical fault type waveform of the sensor according to the method of fault simulation, calculate its fault waveform characteristics, find out the residual between it and the normal waveform characteristics, and normalize the residual;then, use the modeling method of FPN to establish the correlation analysis model between fault types, fault characteristic indicators and fault modes;finally, the establishment of model weights and transition threshold parameters is achieved through the apriori algorithm based on association rules. The maintainer can analyze the fault correlation of the sensor through the abnormal waveform of the sensor to preliminarily judge the fault cause, to achieve the purpose of improving the efficiency of maintenance.

关键词： Sensor Fault causes Fuzzy petri net apriori algorithm Correlation analysis

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A Discovery for the Association Rules of Operation Sequence Based on Improved apriori algorithm

A Discovery for the Association Rules of Operation Sequence ...

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Pacific-Asia Conference on Knowledge Engineering and Software Engineering

作者： Tan Yanhua Wenzhou Univ Coll Phys & Elect Informat Engn Wenzhou Zhejiang Peoples R China

ISBN: (纸本)9783642253485

With the application of the computer aided process design technology, a great deal of process data has been accumulated by enterprises. Inherent linking with these data is found by exploiting apriori algorithm, which will help enterprises with the process design. In this paper, the association rule model of operation sequence is introduced briefly at first. Then the bottleneck problem of apriori algorithm is analysed, on basis of this analysis, an improved apriori algorithm is provided. At last, combining with actual data, a detailed description of the improved apriori algorithm's application in the discovery for association rules of operation sequence is put forward.

关键词： apriori algorithm operation sequence association rule

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Analysis of Coal Mine Hidden Danger Correlation based on Improved apriori algorithm

Analysis of Coal Mine Hidden Danger Correlation based on Imp...

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4th International Conference on Intelligent Systems Design and Engineering Applications (ISDEA)

作者： Liu Shuangyue Peng Li Univ Sci & Technol Sch Civil & Environm Engn Beijing 100083 Peoples R China

ISBN: (纸本)9781479927913

This paper outlines an improved apriori algorithm that suits coal mine hidden danger data. Then preprocess the data according to the characteristics of safety hidden danger data of coal mine workplaces, including data import and extraction. Put this algorithm embedded in a coal mine hidden danger investigation system. After this is coal mine hidden danger data association rules mining based on the improved apriori algorithm to achieve getting valuable association rules inside the coal mine hidden dangers. Provide recommendations for improvement for enterprises in the hidden danger management and prevention and ultimately there is an important practical significance in preventing accidents and reducing the loss of the accidents.

关键词： apriori algorithm coal hidden danger correlation analysis

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Application of apriori algorithm in Meteorological Disaster Information Mining 15th

Application of Apriori Algorithm in Meteorological Disaster ...

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15th International Conference on Cooperative Design, Visualization, and Engineering (CDVE)

作者： Wang, Chunhui Zhao, Boting Fujian Meteorol Serv Ctr Fuzhou Fujian Peoples R China

ISBN: (纸本)9783030005603;9783030005597

With the progresses of meteorological informatization, large amounts of meteorological data were accumulated. For the better application of these data, this research introduced data mining and applied it to analysis meteorological disaster, which can extract implicit, and potential useful information from a large amount of data in order to guide agricultural production. Meteorological disaster data affecting tea production were analyzed based on apriori algorithm in this research, and the strong association rules between meteorological factors and reduction rate of tea yield were found. The results showed that the reduction rate of tea yield is closely related to the daily minimum temperature in the germination stage. When the daily minimum temperature was less than 5 degrees C, the tea would be affected by the cold and freezing damage and the tea yield would decrease, the lower the temperature, the greater the cold and freezing damage.

关键词： Data mining apriori algorithm Meteorological disaster Cold and freezing damage Tea yield

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