Fast Algorithms of Mining Probability Functional Dependency Rules in Relational Database

This paper defines a new kind of rule, probability functional dependency rule. The functional dependency degree can be depicted by this kind of rule. Five algorithms, from the simple to the complex, are presefited to mine this kind of rule in different condition. The related theorems are proved to ensure the high efficiency and the correctness of the above algorithms.

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.

Parallel mining and application of fuzzy association rules

Quantitative attributes are partitioned into several fuzzy sets by using fuzzy c-means algorithm.Fuzzy c-means algorithm can embody the actual distribution of the data,and fuzzy sets can soften the partition boundary.Then,we improve the search technology of apriori algorithm and present the algorithm for mining fuzzy association rules.As the database size becomes larger and larger,a better way is to mine fuzzy association rules in parallel.In the parallel mining algorithm,quantitative attributes are partitioned into several fuzzy sets by using parallel fuzzy c-means algorithm.Boolean parallel algorithm is improved to discover frequent fuzzy attribute set,and the fuzzy association rules with at least a minimum confidence are generated on all processors.The experiment results implemented on the distributed linked PC/workstation show that the parallel mining algorithm has fine scaleup,sizeup and speedup.Last,we discuss the application of fuzzy association rules in the classification.The example shows that the accuracy of classification systems of the fuzzy association rules is better than that of the two popular classification methods:C4.5 and CBA.

Effective Diagnosis of Lung Cancer via Various Data-Mining Techniques

One of the leading cancers for both genders worldwide is lung cancer.The occurrence of lung cancer has fully augmented since the early 19th century.In this manuscript,we have discussed various data mining techniques that have been employed for cancer diagnosis.Exposure to air pollution has been related to various adverse health effects.This work is subject to analysis of various air pollutants and associated health hazards and intends to evaluate the impact of air pollution caused by lung cancer.We have introduced data mining in lung cancer to air pollution,and our approach includes preprocessing,data mining,testing and evaluation,and knowledge discovery.Initially,we will eradicate the noise and irrelevant data,and following that,we will join the multiple informed sources into a common source.From that source,we will designate the information relevant to our investigation to be regained from that assortment.Following that,we will convert the designated data into a suitable mining process.The patterns are abstracted by utilizing a relational suggestion rule mining process.These patterns have revealed information,and this information is categorized with the help of an Auto Associative Neural Network classification method(AANN).The proposed method is compared with the existing method in various factors.In conclusion,the projected Auto associative neural network and relational suggestion rule mining methods accomplish a high accuracy status.

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,ItemArray() 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(direct-addressing 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.

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.

An Overview of Data Mining and Knowledge Discovery

With massive amounts of data stored in databases, mining information and knowledge in databases has become an important issue in recent research. Researchers in many different fields have shown great interest in data mining and knowledge discovery in databases. Several emerging applications in information providing services, such as data warehousing and on-line services over the Internet, also call for various data mining and knowledge discovery techniques to understand user behavior better, to improve the service provided, and to increase the business opportunities. In response to such a demand, this article is to provide a comprehensive survey on the data mining and knowledge discovery techniques developed recently, and introduce some real application systems as well. In conclusion, this article also lists some problems and challenges for further research.

AN EVALUATION APPROACH FOR THE PROGRAM OF ASSOCIATION RULES ALGORITHM BASED ON METAMORPHIC RELATIONS

As data mining more and more popular applied in computer system,the quality as-surance test of its software would be get more and more attention.However,because of the ex-istence of the 'oracle' problem,the traditional test method is not ease fit for the application program in the field of the data mining.In this paper,based on metamorphic testing,a software testing method is proposed in the field of the data mining,makes an association rules algorithm as the specific case,and constructs the metamorphic relation on the algorithm.Experiences show that the method can achieve the testing target and is feasible to apply to other domain.

A Study on Associated Rules and Fuzzy Partitions for Classification

The amount of data for decision making has increased tremendously in the age of the digital economy. Decision makers who fail to proficiently manipulate the data produced may make incorrect decisions and therefore harm their business. Thus, the task of extracting and classifying the useful information efficiently and effectively from huge amounts of computational data is of special importance. In this paper, we consider that the attributes of data could be both crisp and fuzzy. By examining the suitable partial data, segments with different classes are formed, then a multithreaded computation is performed to generate crisp rules (if possible), and finally, the fuzzy partition technique is employed to deal with the fuzzy attributes for classification. The rules generated in classifying the overall data can be used to gain more knowledge from the data collected.

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。

从Fuzzy Taxonomic数值型数据库中挖掘一般化关联规则

挖掘关联规则是数据挖掘研究的一个重要方面 基于属性内通常还存在更高层次的抽象 ,即呈现出Taxonomic结构这一事实 ,Srikant和Agrawal等人提出了在确定的Taxonomic结构下挖掘泛化布尔型关联规则的挖掘算法 但在实际应用中 ,往往这种Taxonomic结构还呈现出模糊性 ;着重研究了在这种模糊Taxonomic结构下如何从数值型数据库中挖掘一般化关联规则的问题 ,提出了一种新的FuzzyTaxonomic数值型数据库模型 ,并提出了相应的规则发现方法 。

基于潜在数据挖掘的小样本数据库对抗攻击防御算法

为了降低小样本数据库欺骗率,提升小样本数据库的攻击防御效果,设计了一种基于潜在数据挖掘的小样本数据库对抗攻击的防御算法(潜在数据挖掘的防御算法).采用改进的Apriori算法,通过频繁属性值集的工作过程获取准确的强关联规则优势,并从小样本数据库中挖掘潜在数据对抗攻击,同时优化候选集寻找频繁集的过程,然后利用关联分析检测对抗攻击,并通过可信度调度控制访问速率来防止产生恶意会话,实现小样本数据库对抗攻击防御.实验结果表明,潜在数据挖掘的防御算法可有效防御小样本数据库遭受的多种类型攻击,降低攻击产生的数据库欺骗率,保障小样本数据库服务器利用率的稳定性.

Mining φ-Frequent Itemset Using FP-Tree

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.

智慧风场+大数据的能源信息管控方法设计

为了解决智慧风场中大量能源信息的存储和处理问题,使用Hadoop大数据框架、分布式关系型数据库和大数据引擎完成数据的存储和转换。采用超声波风速传感器,利用超声波技术计算相应的风速和风向。提出基于Apriori算法的关联规则数据挖掘技术,根据用户需求对数据库进行数据挖掘过程中通过关联规则快速准确地发现类型关联,并通过压缩数据库和删除候选集对Apriori算法进行优化改进。实验结果显示,数据挖掘时间低至1218 ms,加速度比最高可达3.4。

A Fast Algorithm for Mining Association Rules

In this paper, the problem of discovering association rules between items in a large database of sales transactions is discussed, and a novel algorithm, BitMatrix, is proposed. The proposed algorithm is fundamentally different from the known algorithms Apriori and AprioriTid. Empirical evaluation shows that the algorithm outperforms the known ones for large databases. Scale-up experiments show that the algorithm scales linearly with the number of transactions.

基于朴素贝叶斯的大数据模糊随机挖掘仿真

提出基于朴素贝叶斯的大数据模糊随机挖掘仿真方法,为用户挖掘海量数据特征并从中发现可用数据提供有效途径。该方法依据数据间的关联规则,对具备非线性特征的大数据进行融合处理,利用模糊层次聚类算法依据融合后大数据获取大数据语义关联特征;将语义关联特征作为朴素贝叶斯分类器的输入,输出大数据模糊随机挖掘结果。仿真结果表明,上述方法融合大数据时的关联规则支持度最大为100%,大数据融合效果较好;在大数据量为100GB时,其提取大数据语义关联特征时的概率化特征条件引入量高达96%;模糊随机挖掘大数据时,大数据空间聚焦能力较好,可有效实现大数据模糊随机挖掘。

基于特征选择和模糊类支持度的模糊分类关联规则挖掘算法

模糊分类关联规则(Fuzzy Classification Association Rules,FCAR)是一种特殊的模糊关联规则,挖掘FCAR对于构建基于规则的分类模型至关重要。传统关联规则挖掘算法挖掘FCAR时可能会包含较多冗余规则,并且在数据集类别不平衡时,挖掘到的小类规则的数量会急剧减少甚至降为0。为解决上述问题,提出了一种基于特征选择和模糊类支持度-模糊提升度框架(Fuzzy Category Support-Fuzzy Lift Framework,FCS-FLF)的FCAR挖掘算法FSFCS Based FCARMiner(Feature Selection and Fuzzy Category Support-Fuzzy Lift Framework Based FCAR-Miner),基于模糊隶属度矩阵迭代挖掘FCAR。在多个类别不平衡的数据集上的实验结果表明,相比其他算法FSFCS Based FCAR-Miner算法能够避免大量冗余规则的生成,同时也能适应数据类别不平衡的情况,不会出现各类规则数量相差悬殊的情况。

基于国家专利数据库研究中药复方治疗慢性肾衰竭的用药规律

目的:分析国家专利中药复方治疗慢性肾衰竭的用药规律。方法:以国家专利数据库中治疗慢性肾衰竭的中药复方为数据来源,筛选后运用Office Excel 2019建立数据库,再运用IBM SPSS Modeler 18.0、IBM SPSS Statistics 25.0、Cytoscape 3.7.1、中医传承辅助平台V2.5进行用药频数及性味归经统计、关联规则分析、因子分析、复杂网络图构建、熵层次聚类等。结果:纳入治疗慢性肾衰竭的中药复方专利共97首。高频药物有黄芪(48次,49.48%)、大黄(36次,37.11%)、茯苓(27次,27.84%)、白术(25次,25.77%)、丹参(25次,25.77%)等;功效以补虚类、利水渗湿药、清热药、活血化瘀药为主;药性以温、寒、平为主;药味多甘、苦、辛;归经以肝、脾、肺为主;关联分析共获得32组药对组合;因子分析共得到13个公因子;熵层次聚类算法发现候选处方4首。结论:专利中药复方治疗慢性肾衰竭重在补虚,以温阳利水、清热凉血、活血化瘀为主要治法,多选择利水渗湿、补气、清热、活血化瘀、补阳等药物治疗。