基于关系矩阵的关联规则增量式更新

关联规则是当前数据挖掘研究的主要模式之一.本文提出了一种高效的增量式关联规则的挖掘算法USLIG,以处理当最小支持度改变时相应的关联规则的更新问题.该算法通过构建向量之间的关系矩阵,将频繁项目集的产生过程转化为项目集的关系矩阵中向量的运算过程,能充分利用以前的挖掘结果,只需扫描比数据库小得多的向量,克服了IUA及相关算法需多次扫描数据库的缺点.     

一种基于矩阵的多值关联规则的挖掘算法

关联规则是数据挖掘研究的主要模式之一,其中布尔型关联规则的挖掘已经有比较成熟的系统和方法,而多值关联规则的挖掘则不然。本文提出的QARMM算法利用矩阵存储数据,将频繁项目集的产生过程转化为项目集的关系矩阵中向量的运算过程,同时克服了SLIG算法和矩阵算法不能挖掘多值关联规则的弱点,只需运行一次便可挖掘出所有关联规则。实验证明,在等价的数据集上挖掘关联规则,QARMM算法比Apriori算法具有更高的效率。     

基于分解事务矩阵的关联规则挖掘算法

Apriori算法是数据挖掘领域挖掘关联规则频繁项目集的经典算法,但该算法存在产生大量的候选项目集及需要多次扫描数据库的缺陷。为此提出一种新的挖掘关联规则频繁项目集算法（ CApriori算法）：利用分解事务矩阵来压缩存放数据库的相关信息,进而对分解事务矩阵进行关联规则挖掘;优化了由频繁k -1项目集生成频繁k项目集的连接过程;提出了一种不需要扫描数据库,利用行集“与运算”快速计算支持数的方法,改进算法挖掘所有的频繁项目集只需扫描数据库两次。实验结果表明,改进算法在最小支持度较小时效率高于Apriori算法。     

采用布尔映射矩阵的Apriori算法改进研究

针对Apriori算法在数据挖掘过程中需要产生大量的候选集及重复扫描事务数据库等不足,本文基于事务数据库的布尔映射矩阵,提出一种仅需一次扫描数据库的方法。该方法不需要产生候选项集,通过矩阵行交、列交运算及相似度矩阵行交运算,按照项目维度由大到小的反向迭代方法即可发现频繁项集的布尔映射矩阵改进算法(BMM_IA)。研究与实验表明,改进算法节省内存开销、运算速度快,为关联规则挖掘研究与应用提供了新路径。     

一种基于向量的关联规则挖掘算法改进

通过对Apriori算法思想和传统的向量挖掘算法进行分析,提出一种基于向量运算的关联规则改进算法.该算法采用树形数据结构,克服了Apriori算法需多次扫描数据库这一缺点,并通过向量计算来避免生成候选项集,经过实验证明提高了关联规则挖掘的效率.     

大型数据库中关联规则的向量法挖掘

提出一个基于向量运算的崭新的挖掘算法， 它特别适用于并行运算，并且，在整个挖掘过程中，只需扫描数据库一次，而传统的Apriori算法需要多次扫描数据库。因此，数据挖掘效率大大提高。     

基于向量和矩阵的挖掘关联规则的高效算法

挖掘关联规则是数据挖掘中一个重要的课题,产生频繁项目集是其中的一个关键步骤。文章提出了一种基于向量和矩阵的挖掘算法AVM,并将该算法与两种经典的发现频繁项目集的算法进行了比较。该算法只需要对数据库扫描一遍,并且存放辅助信息所需要的空间也少。实验表明与原先的算法相比,该算法的效率较好。     

基于频繁项目集链式存储方法的关联规则算法

为了提高经典关联规则Apriori算法的挖掘效率,针对Apriori算法的瓶颈问题,提出了一种链式结构存储频繁项目集并生成最大频繁项目集的关联规则算法.该算法采用比特向量方式存储事务,生成频繁项目集的同时,把包含此频繁项目的事务作为链表连接到频繁项目之后,生成最大频繁项目集.该算法能够减小扫描事物数据库的次数和生成候选项目集的数量,从而减少了生成最大频繁项目集的时间,实验结果表明,该算法提高了运算效率.     

一种基于向量的概率加权关联规则挖掘算法

关联规则挖掘是数据挖掘领域中最活跃的一个分支。目前提出的许多关联规则挖掘算法需要多次扫描数据库并产生大量候选项集,影响了挖掘效率。针对加权关联规则挖掘算法中多次扫描数据库影响算法性能的问题,对其进行了优化,采取了以空间换时间的思路,提出一种基于向量的概率加权关联规则挖掘算法。以求概率的方式设置项目属性的权值,通过矩阵向量存储结构保存事务记录,只需扫描一次数据库,并且采用不同的剪枝策略及加权支持度和置信度的计算方式。使用数据实例进行模拟实验,结果表明此算法明显提高了挖掘效率。     

一种基于F-矩阵的最大频繁项目集快速挖掘算法

最大频繁项目集挖掘是多种数据挖掘应用研究的一个重要方面,最大频繁项目集的快速挖掘算法研究是当前研究的热点。传统的最大频繁项目集挖掘算法要多遍扫描数据库并产生大量的候选项目集。为此,该文提出了基于F-矩阵的最大频繁项目集快速挖掘算法FMMFIBFM,FMMFIBFM采用FP-tree的存储结构,仅须扫描数据库两遍且不产生候选频繁项目集,有效地提高了频繁项目集的挖掘效率。实验结果表明,FMMFIBFM算法是有效可行的。     

优化处理并行数据库查询的并行数据流方法

本文使用并行数据流技术优化和处理并行数据库查询的方法,提出了一整套相关算法,并给出了一个基于并行数据流方法的并行数据库查询优化处理器的完整设计.这些算法和相应的查询优化处理器已经用于作者自行设计的并行数据库管理系统原型.实践证明,并行数据流方法不仅能够快速有效地实现并行数据库管理系统,也能够有效地进行并行数据库查询的优化处理.     

Efficient prime-based method for interactive mining of frequent patterns

Over the past decade, an increasing number of efficient algorithms have been proposed to mine frequent patterns by satisfying the minimum support threshold. Generally, determining an appropriate value for minimum support threshold is extremely difficult. This is because the appropriate value depends on the type of application and expectation of the user. Moreover, in some real-time applications such as web mining and e-business, finding new correlations between patterns by changing the minimum support threshold is needed. Since rerunning mining algorithms from scratch is very costly and time-consuming, researchers have introduced interactive mining of frequent patterns. Recently, a few efficient interactive mining algorithms have been proposed, which are able to capture the content of transaction database to eliminate possibility of the database rescanning. In this paper, we propose a new method based on prime number and its characteristics mainly for interactive mining of frequent patterns. Our method isolates the mining model from the mining process such that once the mining model is constructed; it can be frequently used by mining process with various minimum support thresholds. During the mining process, the mining algorithm reduces the number of candidate patterns and comparisons by using a new candidate set called candidate head set and several efficient pruning techniques. The experimental results verify the efficiency of our method for interactive mining of frequent patterns.     

Efficient Graph-Based Algorithms for Discovering and Maintaining Association Rules in Large Databases

In this paper, we study the issues of mining and maintaining association rules in a large database of customer transactions. The problem of mining association rules can be mapped into the problems of finding large itemsets which are sets of items brought together in a sufficient number of transactions. We revise a graph-based algorithm to further speed up the process of itemset generation. In addition, we extend our revised algorithm to maintain discovered association rules when incremental or decremental updates are made to the databases. Experimental results show the efficiency of our algorithms. The revised algorithm is a significant improvement over the original one on mining association rules. The algorithms for maintaining association rules are more efficient than re-running the mining algorithms for the whole updated database and outperform previously proposed algorithms that need multiple passes over the database. Received 4 August 1999 / Revised 18 March 2000 / Accepted in revised form 18 October 2000     

Shared-nothing并行事务数据库系统中规则的挖掘与更新算法

关联规则是数据挖掘中的一个重要研究内容．本文提出了Shared—nothing并行事务数据库系统(简称SNPDBS)中一种快速的关联规则挖掘算法SNPMAR，并考虑当最小支持度发生变化后SNPDBS中关联规则的高效更新问题，提出了一种有效的关联规则更新算法SNPIUA．     

Mapreduce下改进Skyline的高效算法

目前基于 MapReduce 的 Skyline 算法随着维度增大会陷入维度灾难，不能高效地解决大数据条件下的计算问题。提出高效算法 MRBPS，利用数据间的互不支配特性，通过一个优化轴点对数据集建立区域标识，在 Map 和 Reduce 阶段优先比较每个点的区域标识，将多维比较简化为一维比较，提高了计算效率，通过系统实验证明：此算法在大数据量时能够明显提高计算效率，与现有算法相比具有高效性和可靠性。     

Spatial Data Mining: Database Primitives, Algorithms and Efficient DBMS Support

Spatial data mining algorithms heavily depend on the efficient processing of neighborhood relations since the neighbors of many objects have to be investigated in a single run of a typical algorithm. Therefore, providing general concepts for neighborhood relations as well as an efficient implementation of these concepts will allow a tight integration of spatial data mining algorithms with a spatial database management system. This will speed up both, the development and the execution of spatial data mining algorithms. In this paper, we define neighborhood graphs and paths and a small set of database primitives for their manipulation. We show that typical spatial data mining algorithms are well supported by the proposed basic operations. For finding significant spatial patterns, only certain classes of paths “leading away” from a starting object are relevant. We discuss filters allowing only such neighborhood paths which will significantly reduce the search space for spatial data mining algorithms. Furthermore, we introduce neighborhood indices to speed up the processing of our database primitives. We implemented the database primitives on top of a commercial spatial database management system. The effectiveness and efficiency of the proposed approach was evaluated by using an analytical cost model and an extensive experimental study on a geographic database.     

Mining closed patterns in multi-sequence time-series databases

In this paper, we propose an efficient algorithm, called CMP-Miner, to mine closed patterns in a time-series database where each record in the database, also called a transaction, contains multiple time-series sequences. Our proposed algorithm consists of three phases. First, we transform each time-series sequence in a transaction into a symbolic sequence. Second, we scan the transformed database to find frequent patterns of length one. Third, for each frequent pattern found in the second phase, we recursively enumerate frequent patterns by a frequent pattern tree in a depth-first search manner. During the process of enumeration, we apply several efficient pruning strategies to remove frequent but non-closed patterns. Thus, the CMP-Miner algorithm can efficiently mine the closed patterns from a time-series database. The experimental results show that our proposed algorithm outperforms the modified Apriori and BIDE algorithms.     

The strong partial transitive-closure problem: algorithms andperformance evaluation

The development of efficient algorithms to process the different forms of transitive-closure (TC) queries within the context of large database systems has recently attracted a large volume of research efforts. In this paper, we present two new algorithms suitable for processing one of these forms, the so called strong partially instantiated transitive closure, in which one of the query's arguments is instantiated to a set of constants and the processing of which yields a set of tuples that draw their values from both of the query's instantiated and uninstantiated arguments. These algorithms avoids the redundant computations and high storage cost found in a number of similar algorithms. Using simulation, this paper compares the performance of the new algorithms with those found in literature and shows clearly the superiority of the new algorithms     

一种自底向上的静态语义查询优化算法

演绎数据库语义查询优化是运用数据库中的语义知识，即完整性约束条件，将用户提交的一种查询转换为能有效执行，并与原查询等价的查询的一种优化方法．至今在这一领域已有了许多的算法，但大多是基于自顶向下的查询计算模式．而本文提出的静态语义查询优化算法及其改进算法是在优化“并”和“连接”操作的过程中进行自底向上的查询计算，因此相对自顶向下的计算方式更有效地提高了查询执行效率．     

并行数据库上的并行CMD－Join算法

并行数据库在多处理机之间的分布方法(简称数据分布方法)对并行数据操作算法的性能影响很大.如果在设计并行数据操作算法时充分利用数据分布方法的特点,可以得到十分有效的并行算法.本文研究如何充分利用数据分布方法的特点,设计并行数据操作算法的问题,提出了基于CMD多维数据分布方法的并行CMD－Join算法.理论分析和实验结果表明,并行CMD－Join算法的效率高于其它并行Join算法.