A Depth-first Algorithm of Finding All Association Rules Generated by a Frequent Itemset

The classical algorithm of finding association rules generated by a frequent itemset has to generate all non-empty subsets of the frequent itemset as candidate set of consequents. Xiongfei Li aimed at this and proposed an improved algorithm. The algorithm finds all consequents layer by layer, so it is breadth-first. In this paper, we propose a new algorithm Generate Rules by using Set-Enumeration Tree (GRSET) which uses the structure of Set-Enumeration Tree and depth-first method to find all consequents of the association rules one by one and get all association rules correspond to the consequents. Experiments show GRSET algorithm to be practicable and efficient.

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.

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 Frequent Generalized Itemsets and Generalized Association Rules Without Redundancy

This paper presents some new algorithms to efficiently mine max frequent generalized itemsets （g-itemsets） and essential generalized association rules （g-rules）. These are compact and general representations for all frequent patterns and all strong association rules in the generalized environment. Our results fill an important gap among algorithms for frequent patterns and association rules by combining two concepts. First, generalized itemsets employ a taxonomy of items, rather than a flat list of items. This produces more natural frequent itemsets and associations such as （meat, milk） instead of （beef, milk）, （chicken, milk）, etc. Second, compact representations of frequent itemsets and strong rules, whose result size is exponentially smaller, can solve a standard dilemma in mining patterns： with small threshold values for support and confidence, the user is overwhelmed by the extraordinary number of identified patterns and associations; but with large threshold values, some interesting patterns and associations fail to be identified. Our algorithms can also expand those max frequent g-itemsets and essential g-rules into the much larger set of ordinary frequent g-itemsets and strong g-rules. While that expansion is not recommended in most practical cases, we do so in order to present a comparison with existing algorithms that only handle ordinary frequent g-itemsets. In this case, the new algorithm is shown to be thousands, and in some cases millions, of the time faster than previous algorithms. Further, the new algorithm succeeds in analyzing deeper taxonomies, with the depths of seven or more. Experimental results for previous algorithms limited themselves to taxonomies with depth at most three or four. In each of the two problems, a straightforward lattice-based approach is briefly discussed and then a classificationbased algorithm is developed. In particular, the two classification-based algorithms are MFGI_class for mining max frequent g-itemsets and EGR_class for mining essential g-rules. The classification-based algorithms are featured with conceptual classification trees and dynamic generation and pruning algorithms.

Multi-Scaling Sampling： An Adaptive Sampling Method for Discovering Approximate Association Rules

One of the obstacles of the efficient association rule mining is theexplosive expansion of data sets since it is costly or impossible to scan large databases, esp., formultiple times. A popular solution to improve the speed and scalability of the association rulemining is to do the algorithm on a random sample instead of the entire database. But how toeffectively define and efficiently estimate the degree of error with respect to the outcome of thealgorithm, and how to determine the sample size needed are entangling researches until now. In thispaper, an effective and efficient algorithm is given based on the PAC (Probably Approximate Correct)learning theory to measure and estimate sample error. Then, a new adaptive, on-line, fast samplingstrategy - multi-scaling sampling - is presented inspired by MRA (Multi-Resolution Analysis) andShannon sampling theorem, for quickly obtaining acceptably approximate association rules atappropriate sample size. Both theoretical analysis and empirical study have showed that the Samplingstrategy can achieve a very good speed-accuracy trade-off.

CFSBC: Clustering in High-Dimensional Space Based on Closed Frequent Item Set

Clustering in high-dimensional space is an important domain in data mining. It is the process of discovering groups in a high-dimensional dataset, in such way, that the similarity between the elements of the same cluster is maximum and between different clusters is minimal. Many clustering algorithms are not applicable to high-dimensional space for its sparseness and decline properties. Dimensionality reduction is an effective method to solve this problem. The paper proposes a novel clustering algorithm CFSBC based on closed frequent itemsets derived from association rule mining, which can get the clustering attributes with high efficiency. The algorithm has several advantages. First, it deals effectively with the problem of dimensionality reduction. Second, it is applicable to different kinds of attributes. Third, it is suitable for very large data sets. Experiment shows that the proposed algorithm is effective and efficient. Key words clustering - closed frequent itemsets - association rule - clustering attributes CLC number TP 311 Foundation item: Supported by the National Natural Science Foundation of China (70371015)Biography: NI Wei-wei (1979-), male, Ph. D candidate, research direction: data mining and knowledge discovery.

A Developed Algorithm of Apriori Based on Association Analysis

A method for mining frequent itemsets by evaluating their probability of supports based on association analysis is presented. This paper obtains the probability of every 1\|itemset by scanning the database, then evaluates the probability of every 2\|itemset, every 3\|itemset, every k \|itemset from the frequent 1\|itemsets and gains all the candidate frequent itemsets. This paper also scans the database for verifying the support of the candidate frequent itemsets. Last, the frequent itemsets are mined. The method reduces a lot of time of scanning database and shortens the computation time of the algorithm.

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.

Backward Support Computation Method for Positive and Negative Frequent Itemset Mining

Association rules mining is a major data mining field that leads to discovery of associations and correlations among items in today’s big data environment. The conventional association rule mining focuses mainly on positive itemsets generated from frequently occurring itemsets (PFIS). However, there has been a significant study focused on infrequent itemsets with utilization of negative association rules to mine interesting frequent itemsets (NFIS) from transactions. In this work, we propose an efficient backward calculating negative frequent itemset algorithm namely EBC-NFIS for computing backward supports that can extract both positive and negative frequent itemsets synchronously from dataset. EBC-NFIS algorithm is based on popular e-NFIS algorithm that computes supports of negative itemsets from the supports of positive itemsets. The proposed algorithm makes use of previously computed supports from memory to minimize the computation time. In addition, association rules, i.e. positive and negative association rules (PNARs) are generated from discovered frequent itemsets using EBC-NFIS algorithm. The efficiency of the proposed algorithm is verified by several experiments and comparing results with e-NFIS algorithm. The experimental results confirm that the proposed algorithm successfully discovers NFIS and PNARs and runs significantly faster than conventional e-NFIS algorithm.

基于Flag-Prefix-Tree的频繁模式挖掘改进算法

稀疏数据集上,条件FP-Tree无法有效压缩且频繁构造开销大,使用伪构造的问题是数据项目未经压缩和过滤导致额外的遍历代价。文章提出了一种简单而新颖的标志前缀树(Flag-Prefix-Tree)和一种新的挖掘稀疏数据集上频繁模式的算法FPT-Mine。通过Flag-Prefix-Tree中的flag,伪构造条件树可以巧妙地过滤不频繁项目。而且flag可以在挖掘过程中递归地重用,只有非常小的开销,但节省了遍历不频繁项目的大量开销。FPT-Mine以自上向下的顺序遍历Flag-Prefix-Tree,并为每个频繁模式创建一个临时根表(Root table)来伪构造条件树,这样就不需要在每个节点上维护父节点和兄弟节点的链接。此外,FPT-Mine在树上应用了合并技术,这使得FlagPrefix-Tree越来越小。研究表明,FPT-Mine在各种稀疏数据集中具有高性能和可扩展性。FPT-Mine在所有测试数据集中的性能都优于FP-growth,当最小支持度阈值降低时,算法之间的差距增大。

频繁项集挖掘研究前沿及展望

频繁项集挖掘是数据挖掘领域的核心任务之一,其目标是发现在数据库中频繁出现的模式。这些模式对于关联规则、分类、异常检测等多个数据挖掘任务都具有重要作用。由于随着项集大小的增加,项集的组合数量呈指数级增长,导致计算复杂性急剧上升,研究人员一直在努力开发高效的算法来解决这一问题。面向频繁项集挖掘的算法、紧凑表示和前沿应用,深入探讨不同技术的的工作原理、优势和局限性,从而对这一领域的研究现状进行全面总结。最后,进一步探讨了该领域的前沿发展趋势,指出计算效率、基于约束的频繁项集挖掘、模式的可解释性以及算法在不同领域的创新应用等未来潜在研究方向。

改进关联规则算法在自然资源云中的应用研究

针对自然资源信息管理分散、网络安全防御能力弱,以及难以追踪溯源威胁攻击行为等问题,本研究在自然资源云中建立了一套安全防护体系,用以整合网络安全资源,强化网络安全态势感知能力,做到攻击敏捷预测、快速回溯。安全防护体系工作效能的提升,核心在于其安全组件检测引擎模块中关联规则算法的改进。首先,在数据采集阶段,通过预处理将威胁告警数据转换为可供机器处理的标准数据格式;其次,在矩阵计算阶段,使用Map Reduce分布式计算框架提升频繁项集的处理效率;最后,以Apriori算法为蓝本,通过单次扫描锁定频繁k项集范围、矩阵向量内积运算、减少冗余候选项集生成三项措施进行算法改进。实验仿真表明:在处理同样容量网络安全多源数据集合,并在相同维度的关联规则矩阵下,本算法处理效率较经典Apriori算法提升3倍以上;随着输入数据集合瞬时容量的逐渐扩增,本算法的时间复杂度稳定,并为增量挖掘算法的一半以下。研究成果可以实现自然资源部网络安全防护工作从传统的“被动挨打”转向“主动防御”的新局面。

Apriori算法的三种优化方法

通过对Apriori算法的思想和性能的分析,认为Apriori算法存在以下三点不足:(1)由K阶频繁集生成K+1阶候选频繁集时,在K+1阶候选频繁集中过滤掉非频繁集的策略值得进一步改进;(2)连接程序中相同的项目重复比较太多,因而其效率值得进一步改进;(3)在回扫数据库时有许多不必比较的项目或事务重复比较。根据上述三点不足,提出了相应的三种优化策略来优化Apriori算法,得到一效率较高的改进Apriori算法。

关联规则挖掘综述

介绍了关联规则挖掘的研究性况,提出了关联规则的分类方法,对一些典型算法进行了分析和秤价,指出传统关系规则衡量标准的不足,归纳出关联规则的价值衡量方,展望了关联规则挖掘的未来研究方向。

基于FP-Tree的最大频繁项目集挖掘及更新算法

挖掘最大频繁项目集是多种数据挖掘应用中的关键问题,之前的很多研究都是采用Apriori类的候选项目集生成-检验方法.然而,候选项目集产生的代价是很高的,尤其是在存在大量强模式和/或长模式的时候.提出了一种快速的基于频繁模式树(FP-tree)的最大频繁项目集挖掘DMFIA(discover maximum frequent itemsets algorithm)及其更新算法UMFIA(update maximum frequent itemsets algorithm).算法UMFIA将充分利用以前的挖掘结果来减少在更新的数据库中发现新的最大频繁项目集的费用.

关联规则挖掘中Apriori算法的研究与改进

经典的产生频繁项目集的Apriori算法存在多次扫描数据库可能产生大量候选及反复对候选项集和事务进行模式匹配的缺陷,导致了算法的效率较低。为此,对Apriori算法进行以下3方面的改进:改进由k阶频繁项集生成k+1阶候选频繁项集时的连接和剪枝策略;改进对事务的处理方式,减少Apriori算法中的模式匹配所需的时间开销;改进首次对数据库的处理方法,使得整个算法只扫描一次数据库,并由此提出了改进算法。实验结果表明,改进算法在性能上得到了明显提高。

一种有效的隐私保护关联规则挖掘方法

隐私保护是当前数据挖掘领域中一个十分重要的研究问题,其目标是要在不精确访问真实原始数据的条件下,得到准确的模型和分析结果.为了提高对隐私数据的保护程度和挖掘结果的准确性,提出一种有效的隐私保护关联规则挖掘方法.首先将数据干扰和查询限制这两种隐私保护的基本策略相结合,提出了一种新的数据随机处理方法,即部分隐藏的随机化回答(randomizedresponsewithpartialhiding,简称RRPH)方法,以对原始数据进行变换和隐藏.然后以此为基础,针对经过RRPH方法处理后的数据,给出了一种简单而又高效的频繁项集生成算法,进而实现了隐私保护的关联规则挖掘.理论分析和实验结果均表明,基于RRPH的隐私保护关联规则挖掘方法具有很好的隐私性、准确性、高效性和适用性.

关联规则挖掘中若干关键技术的研究

Apriori类算法已经成为关联规则挖掘中的经典算法,其技术难点及运算量主要集中在以下两个方面:①如何确定候选频繁项目集和计算项目集的支持数;②如何减少候选频繁项目集的个数以及扫描数据库的次数·目前已提出了许多改进方法来解决第2个问题,并已取得了很好的效果·然而,对于第1个问题,仍沿用Apriori算法中的解决方案,其运算量是较大的·为此,提出了一种基于二进制形式的候选频繁项目集生成和相应的计算支持数算法,该算法只需对挖掘对象进行一些“或”、“与”、“异或”等逻辑运算操作,显著降低了算法的实现难度,将该算法与Apriori类算法相结合,可以进一步提高算法的执行效率,实验结果也表明算法是有效、快速的·

负关联规则的研究

传统的关联规则是A B的形式,将这种形式加以扩展,讨论了A B,A B,A B三种形式,给出了一种负关联规则中支持度与置信度简单有效的计算方法。讨论了同时研究正、负关联规则后出现的矛盾规则问题,提出了用相关性解决这些问题的方法和一种挖掘频繁项集中正、负关联规则的算法,进行了算法的验证实验。实验结果表明,该算法能检测并删除相互矛盾的规则。

Apriori算法的一种优化方法

介绍关联规则挖掘中的经典算法——Apriori算法的关键思想。针对传统Apriori算法效率上的不足,提出一种改进的Apriori算法——En-Apriori算法。该算法采用矩阵的方法,只须扫描一遍数据库,同时优化了连接操作,较好地提高了算法的效率。实验结果表明,En-Apriori算法优于Apriori算法,具有较好的实用性。