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Optimization of fp-growth algorithm based on cloud computing and computer big data

INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2021年第4期12卷 853-863页

作者： Zhang, Baohua Changzhou Vocat Inst Engn Gen Educ Teaching Dept Changzhou 213164 Jiangsu Peoples R China

The rapid development of cloud computing technology has spawned many excellent cloud computing platforms. These cloud computing platforms provide an effective solution for the processing of big data, which can be used as the basis for the study of parallel mining algorithms and the application of algorithms. This article uses the fp-growth algorithm to mine and analyze computer big data. Aiming at the low extraction efficiency of traditional fp-growth algorithm in large-scale data environment, an improved fp-growth algorithm is proposed. In addition, in view of the shortcomings of frequent lists of L elements that are often cross-referenced in the fp-tree construction process, an improved algorithm based on hash tables is proposed, which realizes the storage address processing element name key, and then realizes the element name key to storage numbered mapping. This article mainly introduces the optimization of fp-growth algorithm under the background of cloud computing and computer big data. The experimental results in this paper show that the performance of the improved fp-gtowth algorithm is better than the original algorithm, the traversal time is reduced by 13%, and the mining efficiency is increased by 25%. In addition, the use of this algorithm for data clustering reduces the error rate and optimizes performance becomes better and has better application value.

关键词： Cloud computing EI Computer big data fp-growth algorithm Big data clustering algorithm Optimized design

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S-fpG: A Parallel Version of fp-growth algorithm under Apache Spark™ 2

S-FPG: A Parallel Version of FP-Growth Algorithm under Apach...

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2nd IEEE International Conference on Cloud Computing and Big Data Analysis (ICCCBDA)

作者： Gassama, Aissatou Diaby Dite Camara, Fode Ndiaye, Samba Cheikh Anta Diop Univ Dept Math Dakar Senegal Alioune Diop Univ Dept Math Bambey Senegal

ISBN: (纸本)9781509044993

Frequent Itemsets Mining (FIM) is an essential data mining task, with many real world applications such as market basket analysis, outlier detection, and so one. Many efficient single-node FIM algorithms such as the well-known fp-growth algorithm have been proposed in the last two decades. However, as large-scale datasets are usually adopted nowadays, these algorithms become inefficient to mine frequent itemsets over big data. Scalable parallel algorithms hold the key to solving the problem in this context. However, the existing parallel versions of fp-growth algorithm implemented with the disk-based MapReduce model are not efficient enough for iterative computation. In this paper, we propose an implementation of scalable parallel fp-growth using the inmemory parallel computing framework Apache Spark (TM). Our experimental results demonstrated that the proposed algorithm can scale well and efficiently process large datasets.

关键词： frequent itemset mining fp-growth algorithm parallel computing apache spark (TM)

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Network attacks identification method of relay protection devices communication system based on fp-growth algorithm 4

Network attacks identification method of relay protection de...

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4th IEEE Sustainable Power and Energy Conference (ISPEC)

作者： Gao, Honghui Shi, Zebing Wu, Fangying Yu, Jiang Xu, Quan He, Hongzi Huang, Zhihua China Southern Power Grid Dispatch & Control Ctr Guangzhou Peoples R China Beijing Sifang Engn Co Ltd Beijing Peoples R China CSG Elect Power Res Inst Guangzhou Peoples R China CSG EHV Power Transmiss Co Dali Bureau Dali Peoples R China Yunnan Power Grid Co Ltd Kunming Power Supply Bur Kunming Peoples R China

ISBN: (纸本)9781665485227

In order to ensure the stable operation of relay protection device and identify untrusted data code attacks, this paper proposes a network attacks identification method for relay protection devices communication system based on frequent pattern-growth(fp-growth) algorithm. Firstly, the transmission model of the communication system of the relay protection device is analyzed. The physical side and the communication side of the relay protection device are monitored through the real-time simulation platform to obtain the original data. Then, the original data are discretely quantized and encoded to generate the abnormal event sequence. Then, fpgrowth algorithm is used to analyze the association of the event sequence to obtain the longest and most frequent sequences. The new attacks categories and fault points are compared by the existing frequent sequence characteristics to determine the type of network attacks or the location of fault points, so as to realize the identification of malicious code attacks, and provide a theoretical basis for the application of trusted technology.

关键词： relay protection device network attacks identification trusted technology fp-growth algorithm

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Mining research on correlation factors of residential electricity stability based on improved fp-growth algorithm

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MANAGEMENT OF ENVIRONMENTAL QUALITY 2024年第3期35卷 547-566页

作者： Pan, Hua Liu, Rong Shanghai Univ Elect Power Shanghai Peoples R China

PurposeOn the one hand, this paper is to further understand the residents' differentiated power consumption behaviors and tap the residential family characteristics labels from the perspective of electricity stability. On the other hand, this paper is to address the problem of lack of causal relationship in the existing research on the association analysis of residential electricity consumption behavior and basic information ***/methodology/approachFirst, the density-based spatial clustering of applications with noise method is used to extract the typical daily load curve of residents. Second, the degree of electricity consumption stability is described from three perspectives: daily minimum load rate, daily load rate and daily load fluctuation rate, and is evaluated comprehensively using the entropy weight method. Finally, residential customer labels are constructed from sociological characteristics, residential characteristics and energy use attitudes, and the enhanced fp-growth algorithm is employed to investigate any potential links between each factor and the stability of electricity *** with the original fp-growth algorithm, the improved algorithm can realize the excavation of rules containing specific attribute labels, which improves the excavation efficiency. In terms of factors influencing electricity stability, characteristics such as a large number of family members, being well employed, having children in the household and newer dwelling labels may all lead to poorer electricity stability, but residents' attitudes toward energy use and dwelling type are not significantly associated with electricity ***/valueThis paper aims to uncover household socioeconomic traits that influence the stability of home electricity use and to shed light on the intricate connections between them. Firstly, in this article, from the perspective of electricity stability, the characteristics of the power consumption of residents

关键词： Electricity consumption stability Characteristic labels fp-growth algorithm Data mining

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Employment Skill Recommendation of College Students Based on Parallel fp-growth algorithm 2

Employment Skill Recommendation of College Students Based on...

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2nd IYSF Academic Symposium on Artificial Intelligence and Computer Engineering

作者： Xu Yingzhuo Wang Yifan Xian Shiyou Univ Sch Comp Sci Xian Peoples R China

ISBN: (数字)9781510650336

ISBN: (纸本)9781510650336;9781510650329

The degree of matching between the skills acquired by college students when applying for jobs and the skills required by companies for online recruitment of talents is low, resulting in a low employment rate. In response to this problem, it is proposed to apply the Spark-based parallel fp-growth algorithm to the analysis of job requirements for recruitment information, extract the job skills corresponding to the job, and improve the matching degree between graduates and the recruitment market demand. Compared with the traditional fp-growth algorithm, the Spark-based parallel fp-growth algorithm has better mining for massive data sets.

关键词： component College students employment skills fp-growth algorithm parallel

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An Empirical Analysis and Comparison of Apriori and fp-growth algorithm for Frequent Pattern Mining

An Empirical Analysis and Comparison of Apriori and FP-Growt...

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International Conference on Advanced Communication Control and Computing Technologies*

作者： Singh, Avadh Kishor Kumar, Ajeet Maurya, Ashish K. Shri Ramswaroop Mem Univ Dept Comp Sci & Engn Tindola UP India

ISBN: (纸本)9781479939145

In this paper, we determine the empirical comparison of Apriori and fp-growth algorithm for frequent item set sequences for Web Usage data. We define the data structure, its implementation and algorithmic features mainly focusing on those that also arise in frequent item set mining. Web usage mining itself can be defined further depending on the type of usage data is considered like web server data, application server data and application level data. User logs that are collected at web server are also known as web server data. Some of the characteristic data collected at a web server include IP addresses of users, page references, and access time of the users and these are the main input to the present research. The comparison of algorithm concentrates on web usage mining and particularly focuses on determining the web usage patterns of websites from the server log files. In our analysis we take into empirical comparison for properties like memory size, input data, pre-fetching, scalability and processing efficiency etc, in order to better understand the results of the evaluation.

关键词： web usage mining apriori algorithm fp-growth algorithm fp tree minimum support association rule

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APT Attack Behavior Pattern Mining using the fp-growth algorithm 14

APT Attack Behavior Pattern Mining using the FP-Growth Algor...

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14th IEEE Annual Consumer Communications and Networking Conference (CCNC)

作者： Lee, Mungyu Choi, Junho Choi, Chang Kim, Pankoo Chosun Univ Dept Software Convergence Engn Gwangju South Korea Chosun Univ Div Undeclared Majors Gwangju South Korea Chosun Univ Dept Comp Engn Gwangju South Korea

ISBN: (纸本)9781509061969

There are continuous hacking and social issues regarding APT (Advanced Persistent Threat - APT) attacks and a number of antivirus businesses and researchers are making efforts to analyze such APT attacks in order to prevent or cope with APT attacks, some host PC security technologies such as firewalls and intrusion detection systems are used. Therefore, in this study, malignant behavior patterns were extracted by using an API of PE files. Moreover, the fp-growth algorithm to extract behavior information generated in the host PC in order to overcome the limitation of the previous signature-based intrusion detection systems. We will utilize this study as fundamental research about a system that extracts malignant behavior patterns within networks and APIs in the future

关键词： APT Attack Pattern Mining fp-growth algorithm

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A fp-growth algorithm Based Fault Analysis Method for Distribution Terminal Unit

A FP-Growth Algorithm Based Fault Analysis Method for Distri...

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IEEE/IAS Conference on Industrial and Commercial Power System Asia (IEEE I and CPS Asia)

作者： Zhang, Xuecen Liu, Qiang Tang, Yi Liu, Guofeng Ning, Xin Chen, Jiankun Southeast Univ Sch Cyber Sci & Engn Nanjing Peoples R China Nanjing Power Supply Co State Grid Corp China Nanjing Peoples R China Southeast Univ Sch Elect Engn Nanjing Peoples R China State Grid Corp China Beijing Peoples R China

ISBN: (纸本)9781665434980

Distribution terminal unit (DTU) is an indispensable device that monitors and controls other secondary-side devices in distribution network. With the large-scale integration of DTU into power system, limited numbers of maintenance engineers can hardly cope with the pressure brought about by the massive increment of DTU devices. Fault of DTU may lead to severe impact on normal operation of distribution network and power system. Therefore, researching DTU fault mechanisms and taking measures to lower fault incidence are crucial for ensuring the stable and safe operation of distribution network. In this paper, we proposed a fp-growth algorithm-based fault analysis method for DTU. Firstly, different possible factors that lead to DTU fault are analyzed, fault types are also introduced. Then the main idea of fp-growth algorithm is illustrated. Afterwards, a DTU fault analysis method based on fp-growth algorithm is put forward. Experiment is conducted on realistic DTU fault database to prove the practicality of this method.

关键词： distribution terminal unit fault analysis fault mechanism fp-growth algorithm

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A Behavioral Trust Model for Internet of Healthcare Things using an Improved fp-growth algorithm and Naive Bayes Classifier 3

A Behavioral Trust Model for Internet of Healthcare Things u...

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3rd International Conference on Sustainable Technologies for Industry 4.0 (STI)

作者： Azad, Saiful Saleh, Amin Salem Mahmud, Mufti Kaiser, M. Shamim Miah, Md Saefullah Green Univ Bangladesh Dept Comp Sci & Engn Dhaka Bangladesh Univ Malaysia Pahang Coll Comp & Appl Sci Fac Comp Pekan Malaysia Nottingham Trent Univ Dept Comp Sci Nottingham NG11 8NS England Jahangirnagar Univ Inst Informat Technol Dhaka 1342 Bangladesh

ISBN: (纸本)9781665400091

Healthcare 4.0 has revolutionized the delivery of healthcare services during the last years. Facilitated by it, many hospitals have migrated to the paradigm of being smart. Smartization of hospitals has reduced healthcare costs while providing improved and reliable healthcare services. Thanks to the Internet of Healthcare Things (IoHT) based healthcare delivery frameworks, integration of many heterogeneous devices with varying computational capabilities has been possible. However, this introduced a number of security concerns as many secure communication protocols for traditional networks can not be verbatim employed on these frameworks. To ensure security, the threats can largely be tackled by employing a Trust Management Model (TMM) which will critically evaluate the behavior or activity pattern of the nodes and block the untrusted ones. Towards securing these frameworks through an intelligent TMM, this work proposes a machine learning based Behavioral Trust Model (BTM), where an improved Frequent Pattern growth (ifpgrowth) algorithm is proposed and applied to extract behavioral signatures of various trust classes. Later, these behavioral signatures are utilized in classifying incoming communication requests to either trustworthy and untrustworthy (trust) class using the Naive Bayes classifier. The proposed model is tested on a benchmark dataset along with other similar existing models, where the proposed BMT outperforms the existing TMMs.

关键词： Internet of things secure healthcare framework fp-growth algorithm Naive Bayes classifier.

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Distribution analysis of Pulmonary diseases in Traditional Chinese medicine based on fp-growth algorithm

Distribution analysis of Pulmonary diseases in Traditional C...

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IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM)

作者： Tian, Shuanggui Xiao, Yong Shen, Shaowu Hubei Univ Chinese Med Sch Informat Engn Wuhan Peoples R China

ISBN: (纸本)9781728162157

Objective: to analyze the distribution of pulmonary diseases in TCM and to find out the characteristics and rules in season, sex, age and region. Methods: In this paper, the thought and steps of fp-growth algorithm were analyzed. Meanwhile, correlation analysis was conducted on 5229 pulmonary disease data. Results: In summer, the incidence of male pulmonary diseases is higher than that of female, young than middle-aged or old, and the incidence of cough is higher than that of other pulmonary diseases. Conclusion: According to the distribution law of pulmonary diseases, this paper puts forward the idea of treating pulmonary diseases according to three factors, such as time, region and person.

关键词： fp-growth algorithm Pulmonary diseases distribution of disease

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