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Research on Automatic Mining Method of Behavior Rule Based on Apriori Algorithm 认领 引用
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作者 ZHU Aiqun LU Jin 《US-China Education Review(A)》 2026年第5期295-302,共8页
Behaviour rule mining extracts valuable patterns from large amounts of behavioural data,which is crucial for analysing user behaviour,monitoring systems,and detecting security threats.Traditional manual or statistical... Behaviour rule mining extracts valuable patterns from large amounts of behavioural data,which is crucial for analysing user behaviour,monitoring systems,and detecting security threats.Traditional manual or statistical methods often fail to reveal complex,hidden associations.This study therefore proposes an automated behaviour rule mining method based on an improved Apriori algorithm.This method adapts data preprocessing and feature encoding to behavioural characteristics,introduces an adaptive support threshold and incremental updating to enhance efficiency,and automates the generation and filtering of association rules from frequent behavioural sequences.When evaluated using accuracy,recall,and interpretability metrics on public user behaviour data and simulated system logs,the method was found to effectively mine meaningful rules while maintaining high efficiency with large-scale data.This work offers a scalable and interpretable approach to automated behaviour rule mining that supports intelligent analysis and decision-making. 展开更多
关键词 Apriori algorithm behavior mining frequent patterns association rules automated analysis data mining
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Construction of a Big Data-Based Engineering Cost Prediction Model 认领 引用
2
作者 ZHOU Lina 《外文科技期刊数据库（文摘版）工程技术》 2026年第2期099-103,共5页
With the continuous expansion and increasing complexity of engineering projects, cost forecasting has become particularly critical in project management. To meet the demands of the big data era, this study utilizes ex... With the continuous expansion and increasing complexity of engineering projects, cost forecasting has become particularly critical in project management. To meet the demands of the big data era, this study utilizes extensive historical engineering data to examine the intrinsic relationships and fluctuation patterns of project costs, covering data collection, preprocessing, and key factor analysis. By employing cutting-edge algorithms for comprehensive comparison and intelligent identification of cost-influencing factors, an efficient cost forecasting methodology was developed, achieving accurate predictions of cost trends. Results demonstrate that this method exhibits high forecasting accuracy across various project scenarios, effectively capturing key cost variation characteristics and providing robust insights for budget preparation and risk control. Practical validation confirms that with sufficient data resources and rigorous analysis of critical factors, cost management precision can be significantly enhanced. Overall, this research not only establishes innovative technical approaches for cost forecasting but also advances the digital transformation of project management, offering substantial practical implications and broad application prospects for improving resource allocation efficiency and optimizing project decision-making. 展开更多
关键词 big data construction cost forecasting model data preprocessing key factor mining intelligent algorithm
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Quantum Algorithm for Mining Frequent Patterns for Association Rule Mining 认领 引用 被引量：1
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作者 Abdirahman Alasow Marek Perkowski 《Journal of Quantum Information Science》 CAS 2023年第1期1-23,共23页
Maximum frequent pattern generation from a large database of transactions and items for association rule mining is an important research topic in data mining. Association rule mining aims to discover interesting corre... Maximum frequent pattern generation from a large database of transactions and items for association rule mining is an important research topic in data mining. Association rule mining aims to discover interesting correlations, frequent patterns, associations, or causal structures between items hidden in a large database. By exploiting quantum computing, we propose an efficient quantum search algorithm design to discover the maximum frequent patterns. We modified Grover’s search algorithm so that a subspace of arbitrary symmetric states is used instead of the whole search space. We presented a novel quantum oracle design that employs a quantum counter to count the maximum frequent items and a quantum comparator to check with a minimum support threshold. The proposed derived algorithm increases the rate of the correct solutions since the search is only in a subspace. Furthermore, our algorithm significantly scales and optimizes the required number of qubits in design, which directly reflected positively on the performance. Our proposed design can accommodate more transactions and items and still have a good performance with a small number of qubits. 展开更多
关键词 Data Mining Association Rule Mining Frequent Pattern Apriori Algorithm Quantum Counter Quantum Comparator Grover’s Search Algorithm
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Mining φ-Frequent Itemset Using FP-Tree 认领 引用 被引量：1
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作者 李天瑞 《Journal of Modern Transportation》 2001年第1期67-74,共8页
The problem of association rule mining has gained considerable prominence in the data mining community for its use as an important tool of knowledge discovery from large scale databases. And there has been a spurt of ... The problem of association rule mining has gained considerable prominence in the data mining community for its use as an important tool of knowledge discovery from large scale databases. And there has been a spurt of research activities around this problem. However, traditional association rule mining may often derive many rules in which people are uninterested. This paper reports a generalization of association rule mining called φ association rule mining. It allows people to have different interests on different itemsets that arethe need of real application. Also, it can help to derive interesting rules and substantially reduce the amount of rules. An algorithm based on FP tree for mining φ frequent itemset is presented. It is shown by experiments that the proposed methodis efficient and scalable over large databases. 展开更多
关键词 data processing databases φ association rule mining φ frequent itemset FP tree data mining
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Backward Support Computation Method for Positive and Negative Frequent Itemset Mining 认领 引用 被引量：1
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作者 Mrinmoy Biswas Akash Indrani Mandal Md. Selim Al Mamun 《Journal of Data Analysis and Information Processing》 2023年第1期37-48,共12页
Association rules mining is a major data mining field that leads to discovery of associations and correlations among items in today’s big data environment. The conventional association rule mining focuses mainly on p... Association rules mining is a major data mining field that leads to discovery of associations and correlations among items in today’s big data environment. The conventional association rule mining focuses mainly on positive itemsets generated from frequently occurring itemsets (PFIS). However, there has been a significant study focused on infrequent itemsets with utilization of negative association rules to mine interesting frequent itemsets (NFIS) from transactions. In this work, we propose an efficient backward calculating negative frequent itemset algorithm namely EBC-NFIS for computing backward supports that can extract both positive and negative frequent itemsets synchronously from dataset. EBC-NFIS algorithm is based on popular e-NFIS algorithm that computes supports of negative itemsets from the supports of positive itemsets. The proposed algorithm makes use of previously computed supports from memory to minimize the computation time. In addition, association rules, i.e. positive and negative association rules (PNARs) are generated from discovered frequent itemsets using EBC-NFIS algorithm. The efficiency of the proposed algorithm is verified by several experiments and comparing results with e-NFIS algorithm. The experimental results confirm that the proposed algorithm successfully discovers NFIS and PNARs and runs significantly faster than conventional e-NFIS algorithm. 展开更多
关键词 Data Mining Positive Frequent Itemset Negative Frequent Itemset Association Rule Backward Support
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A New Algorithm for Mining Frequent Pattern 认领 引用 被引量：4
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作者 李力 靳蕃 《Journal of Southwest Jiaotong University(English Edition)》 2002年第1期10-20,共11页
Mining frequent pattern in transaction database, time series databases, and many other kinds of databases have been studied popularly in data mining research. Most of the previous studies adopt Apriori like candidate ... Mining frequent pattern in transaction database, time series databases, and many other kinds of databases have been studied popularly in data mining research. Most of the previous studies adopt Apriori like candidate set generation and test approach. However, candidate set generation is very costly. Han J. proposed a novel algorithm FP growth that could generate frequent pattern without candidate set. Based on the analysis of the algorithm FP growth, this paper proposes a concept of equivalent FP tree and proposes an improved algorithm, denoted as FP growth * , which is much faster in speed, and easy to realize. FP growth * adopts a modified structure of FP tree and header table, and only generates a header table in each recursive operation and projects the tree to the original FP tree. The two algorithms get the same frequent pattern set in the same transaction database, but the performance study on computer shows that the speed of the improved algorithm, FP growth * , is at least two times as fast as that of FP growth. 展开更多
关键词 data mining algorithm frequent pattern set FP growth
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FPGA-Based Stream Processing for Frequent Itemset Mining with Incremental Multiple Hashes 认领 引用
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作者 Kasho Yamamoto Masayuki Ikebe +1 位作者 Tetsuya Asai Masato Motomura 《Circuits and Systems》 2016年第10期3299-3309,共11页
With the advent of the IoT era, the amount of real-time data that is processed in data centers has increased explosively. As a result, stream mining, extracting useful knowledge from a huge amount of data in real time... With the advent of the IoT era, the amount of real-time data that is processed in data centers has increased explosively. As a result, stream mining, extracting useful knowledge from a huge amount of data in real time, is attracting more and more attention. It is said, however, that real- time stream processing will become more difficult in the near future, because the performance of processing applications continues to increase at a rate of 10% - 15% each year, while the amount of data to be processed is increasing exponentially. In this study, we focused on identifying a promising stream mining algorithm, specifically a Frequent Itemset Mining (FIsM) algorithm, then we improved its performance using an FPGA. FIsM algorithms are important and are basic data- mining techniques used to discover association rules from transactional databases. We improved on an approximate FIsM algorithm proposed recently so that it would fit onto hardware architecture efficiently. We then ran experiments on an FPGA. As a result, we have been able to achieve a speed 400% faster than the original algorithm implemented on a CPU. Moreover, our FPGA prototype showed a 20 times speed improvement compared to the CPU version. 展开更多
关键词 Data Mining Frequent Itemset Mining FPGA Stream Processing
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A Fast Distributed Algorithm for Association Rule Mining Based on Binary Coding Mapping Relation 认领 引用
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作者 CHEN Geng NI Wei-wei +1 位作者 ZHU Yu-quan SUN Zhi-hui 《Wuhan University Journal of Natural Sciences》 EI CAS 2006年第1期27-30,共4页
Association rule mining is an important issue in data mining. The paper proposed an binary system based method to generate candidate frequent itemsets and corresponding supporting counts efficiently, which needs only ... Association rule mining is an important issue in data mining. The paper proposed an binary system based method to generate candidate frequent itemsets and corresponding supporting counts efficiently, which needs only some operations such as ＂and＂, ＂or＂ and ＂xor＂. Applying this idea in the existed distributed association rule mining al gorithm FDM, the improved algorithm BFDM is proposed. The theoretical analysis and experiment testify that BFDM is effective and efficient. 展开更多
关键词 frequent itemsets distributed association rule mining relation of itemsets-binary data
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多源异构数据细节特征并行聚类挖掘 认领 引用
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作者 康耀龙 冯丽露 张景安 《现代电子技术》 北大核心 2026年第17期169-172,177,共4页
为了解决多源异构数据细节特征聚类准确度较低的问题,文中提出一种多源异构数据细节特征并行聚类挖掘方法。使用Apriori(先验算法)挖掘多源异构数据集,同时对Apriori算法在生成候选集的过程进行改进,减少候选集的数据量,提升多源异构数... 为了解决多源异构数据细节特征聚类准确度较低的问题,文中提出一种多源异构数据细节特征并行聚类挖掘方法。使用Apriori(先验算法)挖掘多源异构数据集,同时对Apriori算法在生成候选集的过程进行改进,减少候选集的数据量,提升多源异构数据频繁项集挖掘速度。将K-means聚类结合Map-Reduce形成并行聚类,以频繁项集作为初始聚类中心,通过多个Mapper同时计算多源异构数据与聚类中心的距离,将最近的数据划分为该簇完成第一次聚类。使用Reducer汇总聚类数据,通过计算新的聚类中心返回Mapper,实现迭代,直至Reducer计算聚类中心不再变化,完成多源异构数据细节特征并行聚类挖掘。实验结果表明:改进后的Apriori算法展示了更强大的频繁项集挖掘能力,通过多源异构数据并行聚类挖掘细节特征,可以准确描述数据信息,对多源异构数据进行并行聚类,且类间距离明显,类内数据紧凑,分类效果十分优秀。所提方法能够实现准确的多源异构数据并行聚类。 展开更多
关键词 多源异构数据 细节特征 频繁项集 并行聚类 Apriori算法 数据挖掘
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密文态数据下高效的隐私保护频繁项集挖掘协议 认领 引用
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作者 邱硕 胡杨 +1 位作者 柳亚男 马乐军 《金陵科技学院学报》 2026年第2期26-35,共10页
频繁项集挖掘是海量数据集应用中较为广泛的数据挖掘技术,如何高效且安全地从大规模密文数据中挖掘准确有效的信息是亟待研究的问题之一。结合BGN同态加密算法,提出了一种以强隐私保护为核心的频繁项集挖掘协议PPFIM,同时通过添加噪声... 频繁项集挖掘是海量数据集应用中较为广泛的数据挖掘技术,如何高效且安全地从大规模密文数据中挖掘准确有效的信息是亟待研究的问题之一。结合BGN同态加密算法,提出了一种以强隐私保护为核心的频繁项集挖掘协议PPFIM,同时通过添加噪声集合实现了挖掘项集及查询结果的隐私保护。为适应大规模数据场景,对PPFIM协议进行优化,大幅降低了协议耗时。仿真实验结果表明,在百万级数据规模下,PPFIM协议在并行模式下平均耗时54 min,优化协议的运行耗时能降至14 min以内,性能可有效适用于大规模数据集的挖掘需求。 展开更多
关键词 频繁项集挖掘 密文数据 同态加密 隐私保护
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基于区域供热方案的高级㶲分析模型 认领 引用
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作者 柯一鸿 林艺龙 +2 位作者 林周勇 易秉恒 王炯铭 《电子设计工程》 2026年第2期192-196,共5页
为了评估热电联产系统在区域供热中的性能和效率,构建了基于区域供热方案的高级㶲分析模型。利用普通类间距离准则和加权类间距离准则,挖掘用于供暖潜能评估的频繁项集条件模式基,将条件fg-tree的构建与挖掘过程视为一种递归处理过程。以... 为了评估热电联产系统在区域供热中的性能和效率,构建了基于区域供热方案的高级㶲分析模型。利用普通类间距离准则和加权类间距离准则,挖掘用于供暖潜能评估的频繁项集条件模式基,将条件fg-tree的构建与挖掘过程视为一种递归处理过程。以㶲效率指标和能级平衡系数指标为度量依据,分析了基于热电联产系统的高级㶲分析模型的性能,从而实现热电联产系统中的热能评估。实验结果表明,正常生产工况下,文中模型计算得到的㶲效率为0.65,能级平衡系数为0.82,优于对比方法,并具有更高的评估效率。 展开更多
关键词 大数据频繁项集挖掘算法 热电联产 㶲效率 区域供热
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基于隐结构模型与频繁项集的针灸治疗帕金森病非运动症状选穴规律 认领 引用
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作者 伊伸珑 何启旺 +4 位作者 肖蝶 代诚博 金小轩 李亚楠 万碧江 《中医药导报》 2026年第3期166-172,共7页
目的:探讨针灸治疗帕金森病非运动症状(PD-NMS)患者的辨证取穴规律,为临床治疗PD-NMS患者提供帮助。方法:采用系统性文献检索策略,查询中国知网(CNKI)、中国生物医学文献数据库(CBM)、维普中文期刊服务平台、万方数据知识服务平台(Wanfa... 目的:探讨针灸治疗帕金森病非运动症状(PD-NMS)患者的辨证取穴规律,为临床治疗PD-NMS患者提供帮助。方法:采用系统性文献检索策略,查询中国知网(CNKI)、中国生物医学文献数据库(CBM)、维普中文期刊服务平台、万方数据知识服务平台(Wanfang Data)、PubMed、Embase、Web of Science等中英文数据库中有关针灸治疗PD-NMS的相关文献,提取症状、取穴处方信息构建医案数据库,采用隐结构模型、频繁项集分析等方法,分析针灸治疗PD-NMS的辨证取穴规律。检索时限均为建库至2025年2月28日。结果:系统检索中英文数据库,筛选针灸干预PD-NMS的临床文献46篇。在1 366份病历资料中提取71项症状体征、108个精准穴位定位点及114个穴位配伍方案构建结构化数据库,解析PD-NMS中医证型与取穴模式的对应规律。对症状、腧穴、证型进行频繁项集分析,挖掘出症状-腧穴频繁项集5项,包括五心烦热+腰酸+睡眠障碍+太冲等;证型-症状-腧穴频繁项集5项,包括肝肾阴虚证+五心烦热+腰酸+睡眠障碍+太冲+三阴交等。针灸治疗PD-NMS多以太冲、风池、合谷为主穴。结论:针灸治疗帕金森病非运动症状多以太冲、风池、合谷为主穴,配穴依据临床情况辨证取穴,此可为临床治疗帕金森病非运动症状提供参考。 展开更多
关键词 帕金森病 非运动症状 针灸 隐结构模型 频繁项集 辨证选穴规律 数据挖掘 Apriori算法
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Research and Application on Web Information Retrieval Based on Improved FP-Growth Algorithm 认领 引用 被引量：3
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作者 JIAO Minghai YAN Ping JIANG Huiyan 《Wuhan University Journal of Natural Sciences》 EI CAS 2006年第5期1065-1068,共4页
A kind of single linked lists named aggregative chain is introduced to the algorithm, thus improving the architecture of FP tree. The new FP tree is a one-way tree and only the pointers that point its parent at each n... A kind of single linked lists named aggregative chain is introduced to the algorithm, thus improving the architecture of FP tree. The new FP tree is a one-way tree and only the pointers that point its parent at each node are kept. Route information of different nodes in a same item are compressed into aggregative chains so that the frequent patterns will be produced in aggregative chains without generating node links and conditional pattern bases. An example of Web key words retrieval is given to analyze and verify the frequent pattern algorithm in this paper. 展开更多
关键词 data mining chains FP-growth algorithm frequent pattern aggregative information retrieval
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Fast FP-Growth for association rule mining 认领 引用 被引量：2
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作者 杨明 杨萍 +1 位作者 吉根林 孙志挥 《Journal of Southeast University(English Edition)》 EI CAS 2003年第4期320-323,共4页
In this paper, we propose an efficient algorithm, called FFP-Growth (shortfor fast FP-Growth) , to mine frequent itemsets. Similar to FP-Growth, FFP-Growth searches theFP-tree in the bottom-up order, but need not cons... In this paper, we propose an efficient algorithm, called FFP-Growth (shortfor fast FP-Growth) , to mine frequent itemsets. Similar to FP-Growth, FFP-Growth searches theFP-tree in the bottom-up order, but need not construct conditional pattern bases and sub-FP-trees,thus, saving a substantial amount of time and space, and the FP-tree created by it is much smallerthan that created by TD-FP-Growth, hence improving efficiency. At the same time, FFP-Growth can beeasily extended for reducing the search space as TD-FP-Growth (M) and TD-FP-Growth (C). Experimentalresults show that the algorithm of this paper is effective and efficient. 展开更多
关键词 data mining frequent itemsets association rules frequent pattern tree(FP-tree)
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基于Spark的频繁项集挖掘优化算法 认领 引用
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作者 赵亚丽 刘洁 宁敏 《计算机工程与设计》 北大核心 2026年第8期2332-2338,共7页
为有效解决分布式环境下频繁项集挖掘算法计算开销大、计算节点通信成本高的问题,提出一种基于Spark的频繁项集挖掘优化算法(efficient eclat on Spark,E-SPEclat)。采用位串表示项集与事务集合,利用位运算快速生成候选项集及其事务集合... 为有效解决分布式环境下频繁项集挖掘算法计算开销大、计算节点通信成本高的问题,提出一种基于Spark的频繁项集挖掘优化算法(efficient eclat on Spark,E-SPEclat)。采用位串表示项集与事务集合,利用位运算快速生成候选项集及其事务集合;根据项集前缀支持度的降序对数据进行分组分区,减少节点通信开销并均衡工作负载;根据项集后缀是否为频繁2项集进行深度剪枝,减少事务集合交集计算次数。实验结果表明该算法可以有效提高频繁项集的挖掘效率。 展开更多
关键词 分布式并行计算框架 数据挖掘 频繁项集 位串 数据分区 深度剪枝 广播变量
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An efficient and resilience linear prefix approach for mining maximal frequent itemset using clustering 认领 引用
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作者 M.Sinthuja S.Pravinthraja +3 位作者 B K Dhanalakshmi H L Gururaj Vinayakumar Ravi G Jyothish Lal 《Journal of Safety Science and Resilience》 EI CSCD 2025年第1期93-104,共12页
The numerous volumes of data generated every day necessitate the deployment of new technologies capable of dealing with massive amounts of data efficiently.This is the case with Association Rules,a tool for unsupervis... The numerous volumes of data generated every day necessitate the deployment of new technologies capable of dealing with massive amounts of data efficiently.This is the case with Association Rules,a tool for unsupervised data mining that extracts information in the form of IF-THEN patterns.Although various approaches for extracting frequent itemset(prior step before mining association rules)in extremely large databases have been presented,the high computational cost and shortage of memory remain key issues to be addressed while processing enormous data.The objective of this research is to discover frequent itemset by using clustering for preprocessing and adopting the linear prefix tree algorithm for mining the maximal frequent itemset.The performance of the proposed CL-LP-MAX-tree was evaluated by comparing it with the existing FP-max algorithm.Experimentation was performed with the three different standard datasets to record evidence to prove that the proposed CL-LP-MAX-tree algorithm outperform the existing FP-max algorithm in terms of runtime and memory consumption. 展开更多
关键词 Clustering Data mining Frequent itemset mining Linear prefix tree Maximal frequent itemset mining
一种汽车转弯半径大数据算法模型 认领 引用
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作者 覃星福 邱慕逵 +1 位作者 廖艳辉 何厚来 《内燃机与配件》 2026年第11期83-85,共3页
转弯半径是评价汽车转向性能的重要参数,其统计分析结果是开展车辆可靠性测评、车联网大数据挖掘的重要数据支撑。当前汽车行业在转弯半径统计分析中存在成本高、难以适应大数据应用的问题。结合汽车动力学基本原理与车辆大数据挖掘的... 转弯半径是评价汽车转向性能的重要参数,其统计分析结果是开展车辆可靠性测评、车联网大数据挖掘的重要数据支撑。当前汽车行业在转弯半径统计分析中存在成本高、难以适应大数据应用的问题。结合汽车动力学基本原理与车辆大数据挖掘的实际应用需求,本文提出一种基于阿克曼转向的汽车转弯半径大数据算法模型。该模型仅利用车辆后轮轮速、车辆后轮距及轴距建立转弯半径算法模型。为验证模型的适用性,选取实车采集的轮速数据完成转弯半径计算,并与基于高精度陀螺仪数据和基于车辆ESC数据计算的转弯半径进行对比分析,选取城市、乡村、高速三类工况开展测试验证。测试结果表明,该算法模型计算得到的转弯半径在不同工况下的统计结果均能满足汽车可靠性测评的需求,可作为车辆大数据挖掘中转弯半径分析的依据。 展开更多
关键词 汽车转弯半径 算法模型 阿克曼转向 轮速 大数据挖掘
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Unstructured Big Data Threat Intelligence Parallel Mining Algorithm 认领 引用
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作者 Zhihua Li Xinye Yu +1 位作者 Tao Wei Junhao Qian 《Big Data Mining and Analytics》 EI CSCD 2024年第2期531-546,共16页
To efficiently mine threat intelligence from the vast array of open-source cybersecurity analysis reports on the web,we have developed the Parallel Deep Forest-based Multi-Label Classification(PDFMLC)algorithm.Initial... To efficiently mine threat intelligence from the vast array of open-source cybersecurity analysis reports on the web,we have developed the Parallel Deep Forest-based Multi-Label Classification(PDFMLC)algorithm.Initially,open-source cybersecurity analysis reports are collected and converted into a standardized text format.Subsequently,five tactics category labels are annotated,creating a multi-label dataset for tactics classification.Addressing the limitations of low execution efficiency and scalability in the sequential deep forest algorithm,our PDFMLC algorithm employs broadcast variables and the Lempel-Ziv-Welch(LZW)algorithm,significantly enhancing its acceleration ratio.Furthermore,our proposed PDFMLC algorithm incorporates label mutual information from the established dataset as input features.This captures latent label associations,significantly improving classification accuracy.Finally,we present the PDFMLC-based Threat Intelligence Mining(PDFMLC-TIM)method.Experimental results demonstrate that the PDFMLC algorithm exhibits exceptional node scalability and execution efficiency.Simultaneously,the PDFMLC-TIM method proficiently conducts text classification on cybersecurity analysis reports,extracting tactics entities to construct comprehensive threat intelligence.As a result,successfully formatted STIX2.1 threat intelligence is established. 展开更多
关键词 unstructured big data mining parallel deep forest multi-label classification algorithm threat intelligence
基于ISODATA的电力负荷曲线分类 认领 引用 被引量：9
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作者 李仲恒 刘蓉晖 《上海电力学院学报》 2019年第4期327-332,共6页
迭代自组织数据分析算法(ISODATA)是一种基于统计模式识别的非监督学习动态聚类算法。针对当前各算法初始聚类数取值困难、容易陷入局部最优等问题,介绍了ISODATA的原理和实现步骤,并将此算法应用于负荷分类中。在MATLAB中结合具体日负... 迭代自组织数据分析算法(ISODATA)是一种基于统计模式识别的非监督学习动态聚类算法。针对当前各算法初始聚类数取值困难、容易陷入局部最优等问题,介绍了ISODATA的原理和实现步骤,并将此算法应用于负荷分类中。在MATLAB中结合具体日负荷曲线样本进行聚类分析,结果证明聚类效果较好。将ISODATA与各种传统聚类方法进行了对比实验,比较各种算法的聚类效果、预定聚类数目对算法结果的影响,以及初始聚类中心的选择对结果的影响。对比结果证明,此方法适用于负荷分类的研究。 展开更多
关键词 迭代自组织数据分析算法 聚类 日负荷曲线 曲线识别 大数据 数据挖掘
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Fast Discovering Frequent Patterns for Incremental XML Queries 认领 引用
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作者 PENGDun-lu QIUYang 《Wuhan University Journal of Natural Sciences》 EI CAS 2004年第5期638-646,共9页
It is nontrivial to maintain such discovered frequent query patterns in real XML-DBMS because the transaction database of queries may allow frequent updates and such updates may not only invalidate some existing frequ... It is nontrivial to maintain such discovered frequent query patterns in real XML-DBMS because the transaction database of queries may allow frequent updates and such updates may not only invalidate some existing frequent query patterns but also generate some new frequent query patterns. In this paper, two incremental updating algorithms, FUX-QMiner and FUXQMiner, are proposed for efficient maintenance of discovered frequent query patterns and generation the new frequent query patterns when new XMI, queries are added into the database. Experimental results from our implementation show that the proposed algorithms have good performance. Key words XML - frequent query pattern - incremental algorithm - data mining CLC number TP 311 Foudation item: Supported by the Youthful Foundation for Scientific Research of University of Shanghai for Science and TechnologyBiography: PENG Dun-lu (1974-), male, Associate professor, Ph.D, research direction: data mining, Web service and its application, peerto-peer computing. 展开更多
关键词 XML frequent query pattern incremental algorithm data mining
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