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.

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.

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.

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.

Association rule mining algorithm based on Spark for pesticide transaction data analyses

With the development of smart agriculture,the accumulation of data in the field of pesticide regulation has a certain scale.The pesticide transaction data collected by the Pesticide National Data Center alone produces more than 10 million records daily.However,due to the backward technical means,the existing pesticide supervision data lack deep mining and usage.The Apriori algorithm is one of the classic algorithms in association rule mining,but it needs to traverse the transaction database multiple times,which will cause an extra IO burden.Spark is an emerging big data parallel computing framework with advantages such as memory computing and flexible distributed data sets.Compared with the Hadoop MapReduce computing framework,IO performance was greatly improved.Therefore,this paper proposed an improved Apriori algorithm based on Spark framework,ICAMA.The MapReduce process was used to support the candidate set and then to generate the candidate set.After experimental comparison,when the data volume exceeds 250 Mb,the performance of Spark-based Apriori algorithm was 20%higher than that of the traditional Hadoop-based Apriori algorithm,and with the increase of data volume,the performance improvement was more obvious.

Chimp Optimization Algorithm Based Feature Selection with Machine Learning for Medical Data Classification

Datamining plays a crucial role in extractingmeaningful knowledge fromlarge-scale data repositories,such as data warehouses and databases.Association rule mining,a fundamental process in data mining,involves discovering correlations,patterns,and causal structures within datasets.In the healthcare domain,association rules offer valuable opportunities for building knowledge bases,enabling intelligent diagnoses,and extracting invaluable information rapidly.This paper presents a novel approach called the Machine Learning based Association Rule Mining and Classification for Healthcare Data Management System(MLARMC-HDMS).The MLARMC-HDMS technique integrates classification and association rule mining(ARM)processes.Initially,the chimp optimization algorithm-based feature selection(COAFS)technique is employed within MLARMC-HDMS to select relevant attributes.Inspired by the foraging behavior of chimpanzees,the COA algorithm mimics their search strategy for food.Subsequently,the classification process utilizes stochastic gradient descent with a multilayer perceptron(SGD-MLP)model,while the Apriori algorithm determines attribute relationships.We propose a COA-based feature selection approach for medical data classification using machine learning techniques.This approach involves selecting pertinent features from medical datasets through COA and training machine learning models using the reduced feature set.We evaluate the performance of our approach on various medical datasets employing diverse machine learning classifiers.Experimental results demonstrate that our proposed approach surpasses alternative feature selection methods,achieving higher accuracy and precision rates in medical data classification tasks.The study showcases the effectiveness and efficiency of the COA-based feature selection approach in identifying relevant features,thereby enhancing the diagnosis and treatment of various diseases.To provide further validation,we conduct detailed experiments on a benchmark medical dataset,revealing the superiority of the MLARMCHDMS model over other methods,with a maximum accuracy of 99.75%.Therefore,this research contributes to the advancement of feature selection techniques in medical data classification and highlights the potential for improving healthcare outcomes through accurate and efficient data analysis.The presented MLARMC-HDMS framework and COA-based feature selection approach offer valuable insights for researchers and practitioners working in the field of healthcare data mining and machine learning.

A Novel Incremental Mining Algorithm of Frequent Patterns for Web Usage Mining

Because data warehouse is frequently changing, incremental data leads to old knowledge which is mined formerly unavailable. In order to maintain the discovered knowledge and patterns dynamically, this study presents a novel algorithm updating for global frequent patterns-IPARUC. A rapid clustering method is introduced to divide database into n parts in IPARUC firstly, where the data are similar in the same part. Then, the nodes in the tree are adjusted dynamically in inserting process by ＂pruning and laying back＂ to keep the frequency descending order so that they can be shared to approaching optimization. Finally local frequent itemsets mined from each local dataset are merged into global frequent itemsets. The results of experimental study are very encouraging. It is obvious from experiment that IPARUC is more effective and efficient than other two contrastive methods. Furthermore, there is significant application potential to a prototype of Web log Analyzer in web usage mining that can help us to discover useful knowledge effectively, even help managers making decision.

Linguistic Valued Association Rules

Association rules discovering and prediction with data mining method are two topics in the field of information processing. In this paper, the records in database are divided into many linguistic values expressed with normal fuzzy numbers by fuzzy c-means algorithm, and a series of linguistic valued association rules are generated. Then the records in database are mapped onto the linguistic values according to largest subject principle, and the support and confidence definitions of linguistic valued association rules are also provided. The discovering and prediction methods of the linguistic valued association rules are discussed through a weather example last.

Improved Pattern Tree for Incremental Frequent-Pattern Mining

By analyzing the existing prefix-tree data structure, an improved pattern tree was introduced for processing new transactions. It firstly stored transactions in a lexicographic order tree and then restructured the tree by sorting each path in a frequency-descending order. While updating the improved pattern tree, there was no need to rescan the entire new database or reconstruct a new tree for incremental updating. A test was performed on synthetic dataset T10I4D100K with 100,000 transactions and 870 items. Experimental results show that the smaller the minimum support threshold, the faster the improved pattern tree achieves over CanTree for all datasets. As the minimum support threshold increased from 2% to 3.5%, the runtime decreased from 452.71 s to 186.26 s. Meanwhile, the runtime required by CanTree decreased from 1,367.03 s to 432.19 s. When the database was updated, the execution time of im- proved pattern tree consisted of construction of original improved pattern trees and reconstruction of initial tree. The experiment results showed that the runtime was saved by about 15% compared with that of CanTree. As the number of transactions increased, the runtime of improved pattern tree was about 25% shorter than that of FP-tree. The improved pattern tree also required less memory than CanTree.

基于数据挖掘分析《中国百年百名中医临床家丛书》中急性黄疸型肝炎的证治规律

目的:运用数据挖掘技术分析中国近代百年百名中医名家治疗急性黄疸型肝炎的用药规律。方法:收集《中国百年百名中医临床家丛书》(第1版)中治疗的急性黄疸型肝炎病案,筛选出符合纳入标准的处方,将纳入的处方上传到中医传承辅助平台V2.5,建立纳入处方数据库,采用频数分析、聚类分析、关联规则等数据挖掘技术与方法对纳入处方进行分析。结果:对筛选的200首初诊、复诊处方进行分析,得出近现代百名中医大家治疗急性黄疸型肝炎的常用药物有茵陈、栀子、甘草、茯苓、郁金等,高频药物组合包括栀子-茵陈、郁金-茵陈、大黄-茵陈、茯苓-茵陈、泽泻-茵陈等,新处方包括竹茹-茯苓皮-石菖蒲-半夏-陈皮、白茅根-赤芍-黄连-桑白皮、神曲-白豆蔻-佛手-麦芽-玫瑰花、茵陈-大黄-栀子-当归、青黛-枸杞子-垂盆草-败酱草-姜黄等。结论:以中医传承辅助平台为基础,利用数据挖掘技术发现,近现代百名中医大家治疗急性黄疸型肝炎遵循利湿退黄的治疗法则,体现了从“化湿邪、利小便”来诊治急性黄疸型肝炎的学术思想,符合中医标本兼治的用药原则。

Enhancing Network Intrusion Detection Model Using Machine Learning Algorithms

After the digital revolution,large quantities of data have been generated with time through various networks.The networks have made the process of data analysis very difficult by detecting attacks using suitable techniques.While Intrusion Detection Systems(IDSs)secure resources against threats,they still face challenges in improving detection accuracy,reducing false alarm rates,and detecting the unknown ones.This paper presents a framework to integrate data mining classification algorithms and association rules to implement network intrusion detection.Several experiments have been performed and evaluated to assess various machine learning classifiers based on the KDD99 intrusion dataset.Our study focuses on several data mining algorithms such as;naïve Bayes,decision trees,support vector machines,decision tables,k-nearest neighbor algorithms,and artificial neural networks.Moreover,this paper is concerned with the association process in creating attack rules to identify those in the network audit data,by utilizing a KDD99 dataset anomaly detection.The focus is on false negative and false positive performance metrics to enhance the detection rate of the intrusion detection system.The implemented experiments compare the results of each algorithm and demonstrate that the decision tree is the most powerful algorithm as it has the highest accuracy(0.992)and the lowest false positive rate(0.009).

基于轨迹数据的大规模路网交通拥挤时空关联规则挖掘

提出了K近邻RElim(K neighbor-RElim,KNR)算法和时序K近邻RElim(sequential KNbrRElim,SKNR)算法,利用大规模路网的车辆轨迹数据来挖掘路段拥挤关联规则和拥挤传播时空关联规则。其中KNR算法在RElim算法基础上拓展了空间拓扑约束,可高效从大规模车辆轨迹数据集中挖掘路网中关联性拥挤易发路段,并量化这些路段间拥挤的关联性强度。而SKNR算法进一步以滑动窗口的形式拓展时间维度,可以挖掘出大规模路网中难以直接观测的拥挤传播现象,并追溯拥挤传播路径。以成都路网和车辆轨迹数据的挖掘结果对所提出的算法进行了说明和验证,结果表明了算法的有效性和鲁棒性。

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.

基于Eclat算法的八字门滑坡变形因素关联性分析

针对滑坡监测数据库数据量大,进行关联规则分析需要多次扫描数据库导致运行时间长的问题,将Eclat关联规则算法引入滑坡监测数据挖掘中,通过K-means聚类法和Eclat算法对八字门滑坡的变形进行了分析。通过综合研究,选择了降雨量监测值和库水位监测值中的6种因素进行数据挖掘分析。分别挖掘了3种降雨因子和3种库水位因子与八字门滑坡多测点位移的关联性,并从八字门滑坡时空监测大数据挖掘出的全部关联规则中选择8个具有较高的置信水平的关联规则进行分析,发现降雨和库水位因素影响八字门滑坡运动的有效信息。结果表明,这种数据挖掘方法及其在监测数据研究中的高精度,有望广泛应用于库区堆积滑坡的数据分析和预测。

数据挖掘在图书馆大数据利用中的应用

在图书馆的日常运营中,每天都会产生大量的图书流通数据。这些数据不仅仅是记录读者信息和业务统计的工具,更隐藏着巨大的潜在价值。通过对这些数据的深度挖掘,发现读者的借阅行为、图书分类、学科特点以及读者类型之间存在一定的关联。这些关联对于图书馆优化资源配置、提高资源利用率以及提升服务水平具有重要意义。结合实际经验,首先分析数据挖掘技术在图书馆应用的必要性,然后探讨数据挖掘的基本技术。同时,还提出将数据挖掘技术应用于数字图书馆系统的基本步骤,并深入研究数据挖掘技术在图书馆读者借阅行为分析中的应用。

基于关联规则改进的网络异常数据挖掘方法

传统的网络异常数据挖掘方法在计算网络异常数据与相关核心的距离时存在准确度不高的问题,导致挖掘精度有限,因此研究提出一种基于关联规则算法的改进网络异常数据挖掘方法。首先,初始化网络异常数据关联核心,采用Kmeans算法对网络异常实体数据执行局部搜索和优化;其次,运用关联规则算法精确计算网络异常数据与关联核心之间的距离;最后,确定距离关联核心最远的网络异常数据,以完成挖掘过程。研究结果显示,在挖掘相同数量的网络异常数据时,相较于传统方法,该研究方法能显著增加正确挖掘出的网络异常数据比例,提升对网络异常数据的识别精准度,具有显著优势。

基于关联规则的配电网薄弱点诊断研究

配电网运行过程中,电气设备的薄弱点处常发生各类故障,对配电网可靠性带来了严重影响,因此提出了基于无向FP-network的配电网薄弱点分析诊断方法。首先介绍了关联规则中支持度、置信度和不平衡比三种判断指标的基本概念及实际应用意义;然后介绍了配电网薄弱点故障数据的收集与整理,对后续进行关联规则的故障数据作了预处理,并建立了配电网薄弱点分析模型;最后通过算例分析,验证了无向FP-network在配电网薄弱点分析诊断中的可行性及准确性,且准确度可达90%以上。