Application Analysis of Nursing Students'Grades in Course Relevance Based on Association Rule Mining Algorithm Apriori

By analyzing the correlation between courses in students’grades,we can provide a decision-making basis for the revision of courses and syllabi,rationally optimize courses,and further improve teaching effects.With the help of IBM SPSS Modeler data mining software,this paper uses Apriori algorithm for association rule mining to conduct an in-depth analysis of the grades of nursing students in Shandong College of Traditional Chinese Medicine,and to explore the correlation between professional basic courses and professional core courses.Lastly,according to the detailed analysis of the mining results,valuable curriculum information will be found from the actual teaching data.

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.

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.

A New Hybrid Algorithm for Association Rule Mining

HA （hashing array）, a new algorithm, for mining frequent itemsets of large database is proposed. It employs a structure hash array, ltemArray （ ） to store the information of database and then uses it instead of database in later iteration. By this improvement, only twice scanning of the whole database is necessary, thereby the computational cost can be reduced significantly. To overcome the performance bottleneck of frequent 2-itemsets mining, a modified algorithm of HA, DHA （directaddressing hashing and array） is proposed, which combines HA with direct-addressing hashing technique. The new hybrid algorithm, DHA, not only overcomes the performance bottleneck but also inherits the advantages of HA. Extensive simulations are conducted in this paper to evaluate the performance of the proposed new algorithm, and the results prove the new algorithm is more efficient and reasonable.

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.

The Books Recommend Service System Based on Improved Algorithm for Mining Association Rules

The Apriori algorithm is a classical method of association rules mining.Based on analysis of this theory,the paper provides an improved Apriori algorithm.The paper puts foward with algorithm combines HASH table technique and reduction of candidate item sets to enhance the usage efficiency of resources as well as the individualized service of the data library.

Research on Algorithm for Mining Negative Association Rules Based on Frequent Pattern Tree

Typical association rules consider only items enumerated in transactions. Such rules are referred to as positive association rules. Negative association rules also consider the same items, but in addition consider negated items （i. e. absent from transactions）. Negative association rules are useful in market-basket analysis to identify products that conflict with each other or products that complement each other. They are also very convenient for associative classifiers, classifiers that build their classification model based on association rules. Indeed, mining for such rules necessitates the examination of an exponentially large search space. Despite their usefulness, very few algorithms to mine them have been proposed to date. In this paper, an algorithm based on FP tree is presented to discover negative association rules.

A Fast Distributed Algorithm for Association Rule Mining Based on Binary Coding Mapping Relation

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.

Mining Compatibility Rules from Irregular Chinese Traditional Medicine Database by Apriori Agorithm

This paper aims to mine the knowledge and rules on compatibility of drugs from the prescriptions for curing arrhythmia in the Chinese traditional medicine database by Apriori algorithm. For data preparation, 1 113 prescriptions for arrhythmia, including 535 herbs （ totally 10884 counts of herbs） were collected into the database. The prescription data were preprocessed through redundancy reduction, normalized storage, and knowledge induction according to the pretreatment demands of data mining. Then the Apriori algorithm was used to analyze the data and form the related technical rules and treatment procedures. The experimental result of compatibility of drugs for curing arrhythmia from the Chinese traditional medicine database shows that the prescription compatibility obtained by Apriori algorithm generally accords with the basic law of traditional Chinese medicine for arrhythmia. Some special compatibilities unreported were also discovered in the experiment, which may be used as the basis for developing new prescriptions for arrhythmia.

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.

AN INCREMENTAL UPDATING ALGORITHM FOR MINING ASSOCIATION RULES

In this letter, on the basis of Frequent Pattern(FP) tree, the support function to update FP-tree is introduced, then an Incremental FP (IFP) algorithm for mining association rules is proposed. IFP algorithm considers not only adding new data into the database but also reducing old data from the database. Furthermore, it can predigest five cases to three cases.The algorithm proposed in this letter can avoid generating lots of candidate items, and it is high efficient.

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.

Fast FP-Growth for association rule mining

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.

基于SIF模型与Apriori算法的煤矿顶板事故致因关联分析

为更科学地预防煤矿顶板事故的发生,对煤矿顶板事故致因及其关联规则进行识别十分关键。首先,通过文本挖掘并结合SIF事故致因模型,确定56个影响顶板事故发生的致因;其次,通过构建顶板事故数据库并运用Apriori算法进行顶板事故致因关联规则挖掘;最后,绘制顶板事故致因关联规则复杂网络图,并综合分析顶板事故的核心致因及各致因间的关联规则。结果表明:安全培训教育和安全监督管理、作业人员安全意识淡薄和违反作业规程、当班管理人员在现场的管理不到位和其他事故致因之间有着很高的关联度以及提升度,这些因素是造成煤矿顶板事故发生的核心因素。

基于关联规则数据挖掘的Apriori算法应用分析

基于关联规则的数据挖掘主要用于发现数据集中项目之间的联系。以Apriori算法为核心,利用店铺在线订单作为数据载体,深入挖掘顾客购买商品的历史记录,探究商品之间的关联性。通过分析这些关联规则,商家可以更精准地了解顾客的购物喜好和行为模式,从而制定出更具针对性和实效性的营销策略,有效提升商品的销售业绩。

基于改进Apriori算法的道路运输事故致因分析

为深入研究我国道路运输事故的致因因素,探究各因素之间的相关联系,提出一种改进的关联规则算法,并将其运用于道路运输事故数据分析。首先,将预处理后的数据进行分类,建立多维层体系框架;其次,对影响事故属性的因素进行灰色关联分析,生成新的候选项集;最后,运用考虑定向约束的Apriori算法挖掘关联规则。基于广西壮族自治区2019—2022年道路运输事故数据并对其进行详细分析,结果表明:道路运输事故中夜间时段发生事故的原因大多为疲劳驾驶,由于驾驶员行车速度不当造成追尾事故的发生。与运用传统的Apriori算法相比,该方法生成的无效规则减少了69.15%,准确率提高了49.14%,在保证准确性提升的前提下大大提高算法的效率。

基于半监督竞争聚类和改进Apriori算法的大型火电机组燃烧优化

为消纳大规模新能源并网,火电机组通过数据挖掘进行燃烧优化时需处理更高维度、更大存量的数据,现有无监督聚类/Apriori算法挖掘效率低不适应机组高灵活性运行要求。针对此问题,在无监督聚类算法中引入约束惩罚因子使之转为半监督聚类以提高聚类效率,并基于划分思想对Apriori算法进行改进以避免冗余规则的产生,提高挖掘效率,形成基于半监督竞争聚类与划分关联规则挖掘结合的新数据挖掘算法。以某电厂660 MW机组为例,用新算法进行数据挖掘,得到各运行参数优化值,建立典型样本库实施燃烧优化,并与改进前算法做对比。结果表明:新算法提高了挖掘效率与存储空间利用率,对于大型火电机组的燃烧优化有一定的实际应用价值。

Correlation knowledge extraction based on data mining for distribution network planning

Traditional distribution network planning relies on the professional knowledge of planners,especially when analyzing the correlations between the problems existing in the network and the crucial influencing factors.The inherent laws reflected by the historical data of the distribution network are ignored,which affects the objectivity of the planning scheme.In this study,to improve the efficiency and accuracy of distribution network planning,the characteristics of distribution network data were extracted using a data-mining technique,and correlation knowledge of existing problems in the network was obtained.A data-mining model based on correlation rules was established.The inputs of the model were the electrical characteristic indices screened using the gray correlation method.The Apriori algorithm was used to extract correlation knowledge from the operational data of the distribution network and obtain strong correlation rules.Degree of promotion and chi-square tests were used to verify the rationality of the strong correlation rules of the model output.In this study,the correlation relationship between heavy load or overload problems of distribution network feeders in different regions and related characteristic indices was determined,and the confidence of the correlation rules was obtained.These results can provide an effective basis for the formulation of a distribution network planning scheme.