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.

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.

A Rule Management System for Knowledge Based Data Cleaning

In this paper, we propose a rule management system for data cleaning that is based on knowledge. This system combines features of both rule based systems and rule based data cleaning frameworks. The important advantages of our system are threefold. First, it aims at proposing a strong and unified rule form based on first order structure that permits the representation and management of all the types of rules and their quality via some characteristics. Second, it leads to increase the quality of rules which conditions the quality of data cleaning. Third, it uses an appropriate knowledge acquisition process, which is the weakest task in the current rule and knowledge based systems. As several research works have shown that data cleaning is rather driven by domain knowledge than by data, we have identified and analyzed the properties that distinguish knowledge and rules from data for better determining the most components of the proposed system. In order to illustrate our system, we also present a first experiment with a case study at health sector where we demonstrate how the system is useful for the improvement of data quality. The autonomy, extensibility and platform-independency of the proposed rule management system facilitate its incorporation in any system that is interested in data quality management.

Mining Hierarchical Decision Rules from Hybrid Data with Categorical and Continuous Valued Attributes

Decision rules mining is an important issue in machine learning and data mining.However,most proposed algorithms mine categorical data at single level,and these rules are not easily understandable and really useful for users.Thus,a new approach to hierarchical decision rules mining is provided in this paper,in which similarity direction measure is introduced to deal with hybrid data.This approach can mine hierarchical decision rules by adjusting similarity measure parameters and the level of concept hierarchy trees.

Spatial Multidimensional Association Rules Mining in Forest Fire Data

Hotspots (active fires) indicate spatial distribution of fires. A study on determining influence factors for hotspot occurrence is essential so that fire events can be predicted based on characteristics of a certain area. This study discovers the possible influence factors on the occurrence of fire events using the association rule algorithm namely Apriori in the study area of Rokan Hilir Riau Province Indonesia. The Apriori algorithm was applied on a forest fire dataset which containeddata on physical environment (land cover, river, road and city center), socio-economic (income source, population, and number of school), weather (precipitation, wind speed, and screen temperature), and peatlands. The experiment results revealed 324 multidimensional association rules indicating relationships between hotspots occurrence and other factors.The association among hotspots occurrence with other geographical objects was discovered for the minimum support of 10% and the minimum confidence of 80%. The results show that strong relations between hotspots occurrence and influence factors are found for the support about 12.42%, the confidence of 1, and the lift of 2.26. These factors are precipitation greater than or equal to 3 mm/day, wind speed in [1m/s, 2m/s), non peatland area, screen temperature in [297K, 298K), the number of school in 1 km2 less than or equal to 0.1, and the distance of each hotspot to the nearest road less than or equal to 2.5 km.

我国重要数据认定制度的探索与完善

重要数据认定是国家数据安全监管的基础性制度,明确该制度是维护国家数据安全的必然要求,是落实数据安全保护义务的客观需要,也是促进数据要素价值释放的重要举措。对此,我国相关配套规定和国家标准进行了积极探索,从早期尝试列举重要数据具体范围,到目前已转向规定重要数据认定规则,但尚未形成明确统一的规则体系。完善我国重要数据认定制度,要在厘清重要数据概念的基础上,构建基于风险的认定规则体系,主要包括明确重要数据认定的领导体制、细化重要数据认定的条件标准、厘清重要数据认定的疑难界分以及健全重要数据认定的程序机制。

Ethics Lines and Machine Learning: A Design and Simulation of an Association Rules Algorithm for Exploiting the Data

Data mining techniques offer great opportunities for developing ethics lines whose main aim is to ensure improvements and compliance with the values, conduct and commitments making up the code of ethics. The aim of this study is to suggest a process for exploiting the data generated by the data generated and collected from an ethics line by extracting rules of association and applying the Apriori algorithm. This makes it possible to identify anomalies and behaviour patterns requiring action to review, correct, promote or expand them, as appropriate.

基于数据挖掘的痛经用药规律研究

目的:基于数据挖掘技术分析《中医方剂大辞典》中治疗痛经方剂的用药规律。方法:根据纳入与排除标准筛选出《中医方剂大辞典》中治疗痛经的方剂,对其进行方药名称规范,运用Excel、IBM SPSS Modeler 18.0、IBM SPSS Statistics 26.0等进行频数统计、关联规则分析、聚类分析及因子分析。结果:共纳入151首治疗痛经的方剂,涉及中药191味;其中出现频次≥10的中药为当归、川芎、延胡索、白芍、香附、甘草等共36味,药效以补虚药、活血化瘀药、理气药为主,药性偏温、寒、平,药味则偏辛、苦、甘,多入肝、脾、心经。选取出现频次≥25的13味高频药物进行关联规则分析,得到“当归-川芎“”当归-香附-川芎“”当归-熟地黄-白芍-川芎”等常用药对21组,并通过提升关联规则的支持度得到“当归-川芎-白芍”核心组合。进行高频药物聚类分析得出4类药物组合,C1:白芍、熟地黄、川芎、当归;C2:牡丹皮、茯苓;C3:香附、甘草;C4:桃仁、红花,因子分析得到5个公因子。结论《:中医方剂大辞典》中治疗痛经以补血活血、化瘀散结、疏肝理气为主。

公共数据开放的逻辑意蕴:现状考察、问题检视与法治进路

[目的/意义]数字法治政府是与数字中国、数字社会相适应的政府治理新样态。数据要素已成为数字时代经济高质量发展的重要驱动力,而公共数据开放则是数字法治政府建设的重要议题。[方法/过程]文章选取中央及浙江、山东、贵州、广东、四川、福建、广西、海南、江西、江苏10个省份的法规政策和技术标准作为研究样本,对当前我国公共数据开放法规政策体系进行考察,并在此基础上检视数据开放中存在的问题。[结果/结论]研究发现,我国公共数据开放存在内涵边界存在不确定性、法规政策体系存在缺口、行政法治制度供给欠缺、公民数据权利保障机制尚未成熟等问题。为此,应明确公共数据概念界定和权属性质、完善公共数据开放法规政策体系、建构公共数据开放行政法治秩序、健全公民数据权利保障体制机制,进而将公共数据开放融入法治轨道之中。

RTAs框架下跨境数据流动规则对数字服务贸易的影响研究

文章基于2010—2021年全球50个代表性经济体的双边数字服务出口数据,采用引力模型实证检验了跨境数据流动规则的贸易创造效应。结果表明:缔约国之间签订的RTAs中,跨境数据流动规则的承诺水平越高,双边数字服务贸易规模越大,贸易创造效应越显著;将跨境数据流动规则细分为数据流动规则、数据本地化规则和数据保护规则后发现,数据本地化条款对数字服务贸易的促进作用更强;相较于其他部门,跨境数据流动规则对金融服务的贸易促进作用更强;缔约国整体的经济自由度水平越高,跨境数据流动规则对数字服务贸易的促进效应越小;缔约国之间的数据监管环境差距越大,跨境数据流动规则越能促进双边数字服务贸易发展。机制检验结果表明:跨境数据流动规则能够通过降低贸易成本、缩短制度距离和扩大出口国的双向FDI规模促进数字服务贸易发展。研究结论证实了跨境数据流动规则产生的贸易创造效应,为我国有效制定数字服务贸易开放政策、积极参与全球数字治理提供了经验证据。

基于数据挖掘和网络药理学探讨杨丽新治疗抽动障碍用药规律和作用机制

目的 探讨杨丽新教授治疗抽动障碍的用药规律及作用机制。方法 搜集杨丽新教授2016年于广东省中医院儿科门诊治疗抽动障碍的病历资料,运用“中医传承辅助平台(V2.5)”对中药数据进行频数分析和关联度分析,获取核心组方,并根据结果进行网络药理学分析。用TCMSP、ETCM、TCMID、Batman等数据库筛选核心处方中药活性成分,利用Genecard、Drugbank等数据库获取抽动障碍相关疾病靶点并生成韦恩图,得到药物-疾病交集靶点并上传至STRING,使用Cytoscape 3.7.2构建核心网络。运用Metascape数据库对共同靶点进行GO功能及KEGG通路富集分析。运用AutoDock vina和Pyrx软件进行分子对接,进一步筛选核心处方治疗抽动障碍的核心靶点。结果 共录入3 443个病例,处方涉及77味中药,高频药物10味,寒性药物运用最多,多归肺经、脾经,性味以辛、甘、苦多见;关联规则得到32条数据,聚类分析得到4组核心组合。7种核心药物(陈皮、甘草、法半夏、竹茹、茯苓、牡蛎及钩藤)中的核心活性成分145个,靶点基因220个,疾病靶点1 290个,药物-疾病共同靶点共58个。GO功能富集条目422条,生物过程304条,细胞过程44条,分子功能74条,生物功能186条,KEGG富集通路68条。主要活性成分有山柰酚、7-甲氧基-2-甲基异黄酮、刺芒柄花素、β-豆甾醇等,作用于SLC6A4、SLC6A3、HTR2A、HTR2C等靶点,通过神经活性配体-受体相互作用、血清素神经突触、cGMP-PKG等关键信号通路进行调节实现。分子对接结果显示主要活性成分与核心靶点有强烈的结合活性。结论 通过数据挖掘、网络药理学分析和分子对接得出杨丽新教授治疗抽动障碍组方用药规律及作用机制,可为治疗抽动障碍的新方组合提供思路。

基于数据挖掘方法探讨脾胃名家李德新教授临床诊疗虚劳疾病规律

目的基于数据挖掘技术探讨李德新教授治疗虚劳疾病的用药规律。方法收集、整理李德新教授临床诊疗虚劳疾病的病例,运用频数、频率、聚类分析、关联分析等方法对病例中的证型诊断和处方用药进行统计分析和数据挖掘,归纳整理后对医案数据进行科学计算。结果共纳入治疗标准医案虚劳741例,挖掘出与疾病相对应的高频症状、高频证型、高频中药,并对中药进行聚类分析得到有效配伍,同时进行症状、证型与相关中药的关联分析。结论利用数据挖掘技术,可初步揭示李德新教授临床诊疗虚劳疾病的规律,从虚劳论治,培补虚劳对于疾病的治疗具有重要意义,有助于更好地传承李德新教授的学术思想,具有较高的临床参考和学习价值。

基于古今医案云平台探究中医治疗失眠的辨证思路及用药规律研究

目的运用古今医案云平台整理并分析中医治疗失眠的辨证思路及用药规律。方法收集古今医案云平台(V2.3.8)名医医案库中名老中医及其书籍中的医案数据,以古今医案云平台(V2.3.8)对用药频次、药味药性归经、舌象脉象等进行统计分析、聚类分析、复杂网络分析。结果共纳入医案794个,涉及中药477味,主要有柴胡、白芍、甘草、酸枣仁、川芎、远志、赤芍、当归、合欢皮、茯苓、郁金、栀子。中药药性以平为主;药味多属甘、苦、辛;主要归肝经。高频中药系统聚类分析聚为4组,第一组:酸枣仁、茯苓;第二组:远志、合欢皮、郁金;第三组:白芍、栀子;第四组:柴胡、川芎、赤芍。药物关联分析得到:柴胡与白芍、柴胡与赤芍的关联性最强,其次是柴胡与川芎。复杂网络分析得到7组中医证候,从高到底依次为:肝阳上亢证,痰热扰心证,肝火扰心证,气滞血瘀证,心脾两虚证,肝肾阴虚证,肝郁气滞证。结论中医治疗失眠以清热助眠为法,平调阴阳为纲,多使用清热安神之品,值得临床参考运用。

数据跨境流通规则博弈与中国应对

如何安全有效促进数据跨境流通和赢得数据跨境流通治理话语权主导权,成为各国博弈的焦点。当前,受各国数据治理理念,核心利益诉求,深层次个人数据安全、国家数据主权、数据跨境自由流通不可兼得的“三元悖论”以及传统数据治理手段制约,国际数据跨境流通领域形成多元治理机制并存、多套治理模板竞争、多轮博弈持续演进的博弈态势,以及数据跨境流通治理碎片化割裂化的局面。当前,我国以国家安全与数据跨境有序自由流动并重为基本特征的数据跨境流通治理主张,不仅契合我国发展实际,而且符合大多数发展中国家的愿望。总体而言,数据跨境流通治理中国方案是一种合意的数据跨境流通治理方案,但在具体实践中我国数据跨境流通治理依然存在一些短板,如有些规定不太合理导致企业数据跨境流通操作成本高、数据跨境流通治理参与度低导致数据跨境流通互操作性较差、与国际高标准数字经贸规则存在衔接障碍等,同时面临国际上异样的声音,甚至存在被污名化、被打压的风险,不利于数据跨境流通治理中国方案效力与国际影响力的提升。为在数据跨境流通治理领域赢得话语权主导权、推进数据跨境流通国际治理,我国应坚持多边主义,推动以联合国和WTO为主导的国际机制建设;推进“数字丝绸之路”建设,扩大中国方案影响力;加强双多边合作,增强数据跨境流通互操作性;对接高标准数字经贸规则,提高数据跨境流通治理能力。

基于数据驱动贝叶斯网络的化工事故风险分析

为减少化工厂风险分析中的主观干预,基于关联规则和贝叶斯网络构建1种数据驱动风险分析模型。该模型涵盖3个任务项,分别为数据集项、关联规则驱动项和贝叶斯网络风险评估项。首先,收集事故报告和事故因素构建事故数据库;其次,将事故数据导入Apriori算法,并根据关联规则的因素相关性确定贝叶斯网络和条件概率表结构;然后,基于事故因素出现频率计算先验概率和条件概率,并采用Fussel-Vesely计算事故因素的敏感度;最后,收集94起危险化学品中毒窒息事故实例,运用数据驱动风险分析模型评估事故因素的影响大小。研究结果可为减少和避免化工事故提供一定参考,有助于提高相关企业的整体安全水平。

An Effective Network Traffic Data Control Using Improved Apriori Rule Mining

The increasing usage of internet requires a significant system for effective communication. To pro- vide an effective communication for the internet users, based on nature of their queries, shortest routing path is usually preferred for data forwarding. But when more number of data chooses the same path, in that case, bottleneck occurs in the traffic this leads to data loss or provides irrelevant data to the users. In this paper, a Rule Based System using Improved Apriori (RBS-IA) rule mining framework is proposed for effective monitoring of traffic occurrence over the network and control the network traffic. RBS-IA framework integrates both the traffic control and decision making system to enhance the usage of internet trendier. At first, the network traffic data are ana- lyzed and the incoming and outgoing data information is processed using apriori rule mining algorithm. After generating the set of rules, the network traffic condition is analyzed. Based on the traffic conditions, the decision rule framework is introduced which derives and assigns the set of suitable rules to the appropriate states of the network. The decision rule framework improves the effectiveness of network traffic control by updating the traffic condition states for identifying the relevant route path for packet data transmission. Experimental evaluation is conducted by extrac- ting the Dodgers loop sensor data set from UCI repository to detect the effectiveness of theproposed Rule Based System using Improved Apriori (RBS-IA) rule mining framework. Performance evaluation shows that the proposed RBS-IA rule mining framework provides significant improvement in managing the network traffic control scheme. RBS-IA rule mining framework is evaluated over the factors such as accuracy of the decision being obtained, interestingness measure and execution time.

基于数据挖掘分析中医药治疗血精的用药规律

目的基于数据挖掘技术总结中医药治疗血精的用药规律。方法检索中国知网、万方数据知识服务平台和维普网3个中文数据库2002年1月至2023年12月有关中医治疗血精的文献,按照纳入及排除标准筛选文献,对纳入的文献进行资料收集和录入。对中药名称进行标准化处理,并利用频率分布、性味归经、关联规则、聚类分析和因子分析等方法对数据进行全面分析。结果共纳入112首方剂,获得198味中药,用药频次总计为1418次,使用频次较高的中药包括牡丹皮、生地黄、黄柏、墨旱莲、甘草等。高频药物多为清热药、补虚药、止血药;药性以寒性为主;药味以甘、苦味为主;归经以肝、肾、心经为主。关联分析获得7组核心药物组合,聚类分析得出5个新的方剂组合,因子分析得到8个公因子,累积方差贡献率为65.67%。结论中医药治疗血精以补虚、清热、止血为主,攻补兼施,标本同治。

Quantum Algorithm for Mining Frequent Patterns for Association Rule Mining

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.