Mining Frequent Sets Using Fuzzy Multiple-Level Association Rules

At present, most of the association rules algorithms are based on the Boolean attribute and single-level association rules mining. But data of the real world has various types, the multi-level and quantitative attributes are got more and more attention. And the most important step is to mine frequent sets. In this paper, we propose an algorithm that is called fuzzy multiple-level association （FMA） rules to mine frequent sets. It is based on the improved Eclat algorithm that is different to many researchers’ proposed algorithms thatused the Apriori algorithm. We analyze quantitative data’s frequent sets by using the fuzzy theory, dividing the hierarchy of concept and softening the boundary of attributes’ values and frequency. In this paper, we use the vertical-style data and the improved Eclat algorithm to describe the proposed method, we use this algorithm to analyze the data of Beijing logistics route. Experiments show that the algorithm has a good performance, it has better effectiveness and high efficiency.

Results of Associated Implication Algebra on a Partial Ordered Set

Some sufficient and necessary conditions that implication algebra on a partial ordered set is associated implication algebra are obtained, and the relation between lattice H implication algebra and associated implication algebra is discussed. Also, the concept of filter is proposed with some basic properties being studied.

Rough Sets Probabilistic Data Association Algorithm and its Application in Multi-target Tracking

A rough set probabilistic data association(RS-PDA)algorithm is proposed for reducing the complexity and time consumption of data association and enhancing the accuracy of tracking results in multi-target tracking application.In this new algorithm,the measurements lying in the intersection of two or more validation regions are allocated to the corresponding targets through rough set theory,and the multi-target tracking problem is transformed into a single target tracking after the classification of measurements lying in the intersection region.Several typical multi-target tracking applications are given.The simulation results show that the algorithm can not only reduce the complexity and time consumption but also enhance the accuracy and stability of the tracking results.

Arterial Hypertension-Related Factors within Custodial Settings of Southern Benin in 2023

Introduction: Hypertension, a non-communicable disease, is a major public health threat worldwide, accounting for a high level of morbidity and mortality. Although it has been extensively published among the general population, further research is needed to understand the reality of hypertension within the custodial setting. This study aimed to investigate the factors associated with arterial hypertension in custodial settings in southern Benin in 2023. Methods: This was a cross-sectional, descriptive, analytical study held in prisons in southern Benin from March to April 2023, involving inmates selected by two-stage random sampling. In the first stage, four prisons out of the six in the southern region of Benin were selected by simple random sampling. In the second stage, the prisoners were selected by systematic random sampling, with the sampling frame being the numbered list of eligible prisoners in each prison selected. Data collected by observation and questionnaire survey were analyzed using Stata 11 software. Hypertension was defined as systolic blood pressure ≥ 140 mmHg and diastolic blood pressure ≥ 90 mmHg. Overweight was defined by a body mass index (weight/height2 (kg/m2) ≥ 25. Factors associated with hypertension were identified by multiple logistic regression, at a 5% threshold of significance. Results: Altogether 336 inmates aged 37.55 ± 1.72 years were surveyed. The prevalence of hypertension in custodial settings in southern Benin in 2023 was 31.32% (95% CI [17.06;52.57]). Associated factors were inmate age (ORa = 3.36 95% CI: [1.94;5.85]) and abnormal waist circumference (ORa = 2.61 95% CI [1.27;5.40]). Conclusion: The prevalence of arterial hypertension in prisons of southern Benin (31.32%) is high when compared with the national average (25.9% (22.5-29.3)). The ministries of the Interior and Health need to collaborate to involve inmates in preventive strategies for non-communicable diseases, including hypertension.

基于Rough Set和灰色关联度的煤炭企业评价方法

针对企业评价中已有评价方法权数分配不合理、指标权数测定困难、难以对企业进行有效的综合评价的现状,采用粗糙集理论和加权灰色关联度的方法测定指标权数,对煤炭企业进行综合评价。以2006年煤炭行业上市公司的财务报表数据为实例进行实证分析,结果表明:基于粗糙集和灰色关联度的企业综合评价方法能够克服传统评价指标权数确定的主观偏好,是一种可行的评价方法。

基于Rough Set带结论域的关联规则挖掘

论文构建了一种基于RoughSet(RS)带结论域的强关联规则挖掘模型,采用约简决策表和改进的Apriori算法来挖掘关联规则,提高了关联规则的挖掘效率和挖掘质量,提出并实现了带结论域的关联规则挖掘的解决方案。

基于DiffNodeset结构的最大频繁项集挖掘算法

在数据挖掘中,通过挖掘最大频繁项集来代替挖掘频繁项集可以大大地提升系统的运行效率。针对现有的最大频繁项集挖掘算法的运行时间消耗仍然很大的问题,提出了一种基于DiffNodeset结构的最大频繁项集挖掘(DNMFIM)算法。首先,采用了一种新的数据结构DiffNodeset来实现求交集以及支持度的快速计算;其次,引入一种新的线性复杂度的连接方法来降低两个DiffNodeset在连接过程中的复杂度,避免了多次的无效计算;然后,将集合枚举树作为搜索空间,同时采用多种优化剪枝策略来缩小搜索空间;最后,再结合最大频繁项集挖掘算法(MAFIA)中所使用的超集检测技术来有效地提高算法的准确性。实验结果表明,DNMFIM算法在时间效率方面性能优于MAFIA与基于N-list的MAFIA(NB-MAFIA),该算法在不同类型数据集中进行最大频繁项集挖掘时均有良好的效果。

Mining association rule efficiently based on data warehouse

The conventional complete association rule set was replaced by the least association rule set in data warehouse association rule mining process. The least association rule set should comply with two requirements: 1) it should be the minimal and the simplest association rule set; 2) its predictive power should in no way be weaker than that of the complete association rule set so that the precision of the association rule set analysis can be guaranteed. By adopting the least association rule set, the pruning of weak rules can be effectively carried out so as to greatly reduce the number of frequent itemset, and therefore improve the mining efficiency. Finally, based on the classical Apriori algorithm, the upward closure property of weak rules is utilized to develop a corresponding efficient algorithm.

Mining association rules in incomplete information systems

Based on the rough set theory which is a powerful tool in dealing with vagueness and uncertainty, an algorithm to mine association rules in incomplete information systems was presented and the support and confidence were redefined. The algorithm can mine the association rules with decision attributes directly without processing missing values. Using the incomplete dataset Mushroom from UCI machine learning repository, the new algorithm was compared with the classical association rules mining algorithm based on Apriori from the number of rules extracted, testing accuracy and execution time. The experiment results show that the new algorithm has advantages of short execution time and high accuracy.

Minimal Critical Sets of Refined Inertias for Irreducible Sign Patterns of Order 2

Let S be a nonempty, proper subset of all refined inertias. Then, S is called a critical set of refined inertias for ireducible sign patterns of order n if is sufficient for any sign pattern A to be refined inertially arbitrary. If no proper subset of Sis a critical set of refined inertias, then S is a minimal critical set of refined inertias for sign patterns of order n . In this paper, all minimal critical sets of refined inertias for irreducible sign patterns of order 2 are identified. As a by-product, a new approach is presented to identify all minimal critical sets of inertias for irreducible sign patterns of order 2.

三氯乙烯对L-02肝细胞中SET相互作用蛋白eEF1A1、eEF1A2和DDB1 mRNA表达水平的影响

目的:探讨人L-02肝细胞经三氯乙烯(TCE)染毒后SET相互作用蛋白e EF1A1、e EF1A2和DDB1 m RNA表达水平的变化。方法:取对数生长期的L-02肝细胞,暴露于不同浓度TCE(1.0、2.0、4.0、8.0 mmol/L)中,以DMSO为溶剂对照。24 h后,采用实时荧光定量PCR检测e EF1A1、e EF1A2和DDB1 m RNA表达水平。结果:与对照组相比,不同浓度的TCE均能够使L-02肝细胞e EF1A1、e EF1A2和DDB1 m RNA表达水平发生明显变化。其中,DDB1和e EF1A2 m RNA表达水平在低浓度(1.0 mmol/L)TCE暴露下显著升高,而随着TCE浓度的升高其m RNA表达水平显著降低(P<0.05或P<0.01)。e EF1A1 m RNA表达水平随着TCE浓度的增加而升高(P<0.05)。结论:TCE染毒可诱发L-02肝细胞中SET相互作用蛋白的m RNA表达水平发生改变。

Improvement of Mining Fuzzy Multiple-Level Association Rules from Quantitative Data

Data-mining techniques have been developed to turn data into useful task-oriented knowledge. Most algorithms for mining association rules identify relationships among transactions using binary values and find rules at a single-concept level. Extracting multilevel association rules in transaction databases is most commonly used in data mining. This paper proposes a multilevel fuzzy association rule mining model for extraction of implicit knowledge which stored as quantitative values in transactions. For this reason it uses different support value at each level as well as different membership function for each item. By integrating fuzzy-set concepts, data-mining technologies and multiple-level taxonomy, our method finds fuzzy association rules from transaction data sets. This approach adopts a top-down progressively deepening approach to derive large itemsets and also incorporates fuzzy boundaries instead of sharp boundary intervals. Comparing our method with previous ones in simulation shows that the proposed method maintains higher precision, the mined rules are closer to reality, and it gives ability to mine association rules at different levels based on the user’s tendency as well.

Numerical Simulation of Wave Height and Wave Set-Up in Nearshore Regions

Based on the time dependent mild slope equation including the effect of wave energy dissipation, an expression for the energy dissipation factor is derived in conjunction with the wave energy balance equation, and then a practical method for the simulation of wave height and wave set- up in nearshore regions is presented. The variation of the complex wave amplitude is numerically simulated by use of the parabolic mild slope equation including the effect of wave energy dissipation due to wave breaking. The components of wave radiation stress are calculated subsequently by new expressions for them according to the obtained complex wave amplitude, and then the depth-averaged equation is applied to the calculation of wave set-up due to wave breaking. Numerical results are in good agreement with experimental data, showing that the expression for the energy dissipation factor is reasonable and that the new method is effective for the simulation of wave set-up due to wave breaking in nearshore regions.

NIA2: A fast indirect association mining algorithm

Indirect association is a high level relationship between items and frequent item sets in data. There are many potential applications for indirect associations, such as database marketing, intelligent data analysis, web -log analysis, recommended system, etc. Existing indirect association mining algorithms are mostly based on the notion of post - processing of discovery of frequent item sets. In the mining process, all frequent item sets need to be generated first, and then they are fihered and joined to form indirect associations. We have presented an indirect association mining algorithm （NIA） based on anti -monotonicity of indirect associations whereas k candidate indirect associations can be generated directly from k - 1 candidate indirect associations, without all frequent item sets generated. We also use the frequent itempair support matrix to reduce the time and memory space needed by the algorithm. In this paper, a novel algorithm （NIA2） is introduced based on the generation of indirect association patterns between itempairs through one item mediator sets from frequent itempair support matrix. A notion of mediator set support threshold is also presented. NIA2 mines indirect association patterns directly from the dataset, without generating all frequent item sets. The frequent itempair support matrix and the notion of using tm as the support threshold for mediator sets can significantly reduce the cost of joint operations and the search process compared with existing algorithms. Results of experiments on a real - word web log dataset have proved NIA2 one order of magnitude faster than existing algorithms.

子宫内膜癌组织set基因与MACC1表达及意义

目的分析set与结肠癌转移相关基因1（MACC1）在不同子宫内膜组织中的表达情况,评价两者在子宫内膜癌发生、发展中的作用。方法 2013年1月—2015年3月,收集本院妇产科行手术治疗的子宫内膜癌癌变内膜标本41例,术后病理确诊为子宫内膜不典型增生组织标本27例,术后病理确诊为正常子宫内膜组织标本30例。采用免疫组织化学染色检测set和MACC1在不同子宫内膜组织中的表达情况,并分析两者与子宫内膜癌临床病理参数间的相关性。结果 set、MACC1在正常子宫内膜组织的阳性表达率分别为3.3%（1/30）、10.0%（3/30）,在内膜不典型增生组织中的阳性表达率分别为40.7%（11/27）、33.3%（9/27）,在子宫内膜癌组织中的阳性表达率分别为68.3%（28/41）、61.0%（16/41）,组间比较差异均有显著意义（χ2=4.655~30.255,P〈0.05）。set、MACC1的表达与子宫内膜癌的临床分期、肌层浸润以及淋巴结转移情况显著相关（χ2=4.188~8.667,P〈0.05）;而与病人年龄和肿瘤分化程度无关（P〉0.05）。set与MACC1在子宫内膜癌组织中的表达呈正相关关系（r=0.413,P〈0.01）。结论 set与MACC1可能协同参与了子宫内膜癌的发生与发展,两者异常高表达可作为判断子宫内膜癌转移与浸润的重要标志;两者的检测对疾病的临床诊断与治疗具有重要意义。

英国工人教育协会课程设置的特色、价值及启示——基于解放教育学视角的研究

英国工人教育协会致力于通过教育改善工人阶级的处境,其课程设置紧密贴合工人阶级的实际需求,在促进工人阶级和整体社会发展方面发挥了重要作用。基于解放教育学理论视角分析发现,英国工人教育协会课程设置具有课程理念尊重人权,关注人性化;课程内容联系实际,强调“生成主题”;课程形式提倡对话,实行导师指导班制度等优势特色。其价值意蕴表现为课程理念助推工人阶级的人性化实现,课程内容培养工人阶级的批判意识以及课程形式促进教育主体之间的对话。有鉴于此,未来我国成人教育课程设置应秉持“以人为本”的课程理念,丰盈满足需求的课程内容,推行提问对话的课程形式。

基于关联规则的群组共享密钥协议研究

静态的密钥管理策略难以适应网络动态变化.针对这一问题提出一种基于关联规则的群组密钥管理方案,使用关联规则分析移动分布式网络中密钥之间的关系,通过路由中密钥使用频率挖掘群组密钥之间的频繁关系,根据可信度建立合理的密钥共享方案.在效率上,该方案适应网络动态变化,降低对先验知识的需求,发现频繁密钥集规模与路由延时之间的折中关系,网络实体可根据密钥之间的频繁关系动态调整群组密钥管理方式.在安全性上,节点根据密钥规则选择密钥策略,在频繁密钥集上实施共享密钥协议,提高了安全性.

基于优先级诊断树的工控网络入侵数据关联挖掘方法

研究基于优先级诊断树的工控网络入侵数据关联挖掘方法,提高工控网络入侵数据挖掘能力。采用网格搜索与模拟退火算法相结合的方法得出SVM提取器最优参数,通过SVM提取器提取工控网络入侵数据特征,运用粗糙集约简工控网络入侵数据特征属性,降低工控网络入侵挖掘的数据样本冗余特征属性。将约简的特征作为优先级诊断树的输入,依据工控网络入侵数据关联挖掘准则,使用优先级诊断树实现工控网络入侵数据关联挖掘。实验结果表明,该方法平均误报率为1.38%,检测率大于90%,挖掘时间低于3.6 s,在-6~26 dB归一化空间谱范围内,均能有效地实现工控网络入侵信号数据挖掘且检测效果最优。

基于分布式多关联属性的高维数据差分隐私保护方法

针对高维数据发布的过程中存在由多关联属性引发的隐私信息泄露风险问题,在分布式环境下提出一种满足差分隐私保护的多关联属性高维数据发布方法(HDMPDP)。根据数据维度,提出一种基于分布式划分的粗糙集高效降维方法,完成对高维复杂数据特征属性的划分,降低数据维度的同时提高处理效率;设计属性分类准则,利用属性信息熵改进关联分析方法;对得到的属性分别进行加噪,优化噪声添加的方式,减轻关联属性带来的隐私问题。在Spark分布式框架下实现隐私保护数据发布,通过高维数据实验验证了该方法的有效性和隐私保护的安全性。