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.

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.

Algorithm of Intrusion Detection Based on Data Mining and Its Implementation

Intrusion detection is regarded as classification in data mining field. However instead of directly mining the classification rules, class association rules, which are then used to construct a classifier, are mined from audit logs. Some attributes in audit logs are important for detecting intrusion but their values are distributed skewedly. A relative support concept is proposed to deal with such situation. To mine class association rules effectively, an algorithms based on FP-tree is exploited. Experiment result proves that this method has better performance.

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.

Research on Employment Data Mining for Higher Vocational Graduates

In order to make effective use a large amount of graduate data in colleges and universities that accumulate by teaching management of work, the paper study the data mining for higher vocational graduates database using the data mining technology. Using a variety of data preprocessing methods for the original data, and the paper put forward to mining algorithm based on commonly association rule Apriori algorithm, then according to the actual needs of the design and implementation of association rule mining system, has been beneficial to the employment guidance of college teaching management decision and graduates of the mining results.

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.

FP-tree关联规则算法在推荐系统中的应用

近几年,由于电子商务迅猛发展,推荐系统逐渐成为了最热门的竞争手段。目前,推荐系统主要包括三个方面的推荐:热卖产品推荐、新产品上市推荐和相关产品推荐等。文中关注相关产品推荐,也就是利用数据挖掘技术在大量的历史销售记录数据中进行挖掘,找出隐藏在不同的商品之间的相关信息,用动态网页的形式向用户推荐。采用FP-tree关联规则算法实现对客户信息的数据挖掘,并将其应用在推荐系统中。

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.

基于改进的FP-tree的频繁模式挖掘算法

FP-growth算法是一种基于FP-tree数据结构的高效的频繁模式挖掘算法,它不产生候选集。构造频繁模式树FP-tree需扫描数据库两次,在第二遍扫描中还扫描了那些仅包含了非频繁项的事务,针对此问题,在深入分析了FP-tree特性的基础上,改进了FP-tree构造过程,同时用一种基于Hash表的辅助存储结构,节省了项目查找时间,提高了挖掘效率。

FP-Tree算法规则挖掘的研究与应用

对FP-Tree算法的规则挖掘以及阈值设定与规则获取的关系进行了研究.选取高校医疗系统中存储的大学生体检数据,并对这些原始数据进行过滤、转换等加工处理,得到便于进行规则挖掘的事务数据库.将事务数据库中的数据用FP-Tree算法进行处理,得到数据之间的关联关系,从而对应获取大学生群体中常见慢性病之间的关联关系.在FP-Tree算法应用过程中设定相关参数的不同阈值,并反复实验调整最小支持度阈值和最小置信度阈值以满足医学标准.所获得的关联关系可以在患某种慢性病的早期就敦促大学生改掉不良嗜好、养成良好的生活习惯,降低严重慢性疾病发生的概率.

高效FP-TREE创建算法

如何从大型数据库中挖掘关联规则是数据挖掘的一个重要的问题。FP-growth是一个著名的不产生候选集的高效频繁模式挖掘算法,它使用专门的数据结构FP-tree。为了进一步提高FP-grown算法效率,提出一个新的并行算法PFPTC,可以并发地创建子FP-tree,以及一个FP-tree合并算法称作FP-merge,可以将两个FP-tree合并为一个。

基于FP-tree算法的推荐系统设计与实现

当前是信息爆炸的时代,推荐系统已成为解决当前网络信息超载的有效工具。文章针对网上书店的电子商务网站的销售特点,详细地设计了推荐系统,并利用挖掘技术中的FP-tree关联规则算法实现数据挖掘运算,很好的实现了在线推荐的系统功能。

一种基于fp-tree的Apriori算法改进研究

提出了一种改进的基于fp-tree的Apriori算法.该算法先用尾元将fp-tree分区,生成数据量更小的子数据集,再动态删除冗余数据将子数据集的数据进一步压缩,最后通过扫描子数据集进行支持数统计,从而快速挖掘.实验结果表明,在对含有大量高维度数频繁项集的数据集进行挖掘时,这个改进算法的挖掘速度较快.

压缩FP-Tree的改进搜索算法

为克服Apriori算法候选频繁项集的支持数计算效率过低和频繁模式增长算法FP-Growth多次建立条件模式树时内存耗费大的问题,提出基于压缩频繁模式树(CFP-Tree)的改进搜索算法(MCFP-Tree)。利用Apriori算法候选项集生成的思想和压缩频繁模式树紧凑的数据结构,采用自底向上的搜索策略,快速挖掘压缩频繁模式树及其子树,更快得到候选项集的支持数。实验结果表明,该算法可以高效计算出候选频繁项集出现的频次,挖掘效率明显优于Apriori和FPGrowth算法。

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.

Adaptive Interval Configuration to Enhance Dynamic Approach for Mining Association Rules

ost proposed algorithms for mining association rules follow the conventional level wise approach. The dynamic candidate generation idea introduced in the dynamic itemset counting (DIC) algorithm broke away from the level wise limitation which could find the large itemsets using fewer passes over the database than level wise algorithms. However, the dynamic approach is very sensitive to the data distribution of the database and it requires a proper interval size. In this paper an optimization technique named adaptive interval configuration (AIC) has been developed to enhance the dynamic approach. The AIC optimization has the following two functions. The first is that a homogeneous distribution of large itemsets over intervals can be achieved so that less unnecessary candidates could be generated and less database scanning passes are guaranteed. The second is that the near optimal interval size could be determined adaptively to produce the best response time. We also developed a candidate pruning technique named virtual partition pruning to reduce the size 2 candidate set and incorporated it into the AIC optimization. Based on the optimization technique, we proposed the efficient AIC algorithm for mining association rules. The algorithms of AIC, DIC and the classic Apriori were implemented on a Sun Ultra Enterprise 4000 for performance comparison. The results show that the AIC performed much better than both DIC and Apriori, and showed a strong robustness.

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.