From Sequential Pattern Mining to Structured Pattern Mining: A Pattern-Growth Approach

Sequential pattern mining is an important data mining problem with broad applications. However,it is also a challenging problem since the mining may have to generate or examine a combinatorially explosivenumber of intermediate subsequences. Recent studies have developed two major classes of sequential patternmining methods: (1) a candidate generation-and-test approach, represented by (i) GSP, a horizontal format-basedsequential pattern mining method, and (ii) SPADE, a vertical format-based method; and (2) a pattern-growthmethod, represented by PrefixSpan and its further extensions, such as gSpan for mining structured patterns. In this study, we perform a systematic introduction and presentation of the pattern-growth methodologyand study its principles and extensions. We first introduce two interesting pattern-growth algorithms, FreeSpanand PrefixSpan, for efficient sequential pattern mining. Then we introduce gSpan for mining structured patternsusing the same methodology. Their relative performance in l  

序列模式挖掘算法研究

数据挖掘领域一个活跃的研究分支就是序列模式的发现,即在序列数据库中找出所有的频繁子序列。目前的序列模式挖掘方法主要分为两类,一类是候选集生成-测试方法;另一类是模式扩展方法。先介绍序列模式挖掘中的基本概念,然后描述几个重要算法,最后给出性能分析。  

一种基于大项集重用的序列模式挖掘算法

在重新定义序列模式的长度、增加了序列模式的挖掘粒度的基础上，提出一种基于大项集重用的序列模式挖掘算法HVSM．该算法采用垂直位图法表示数据库，先横向扩展项集，将挖掘出的所有大项集组成一大序列项集，再纵向扩展序列，将每个一大序列项集作为“集成块”，在挖掘k大序列时重用大项集．并以兄弟节点为种子生成候选大序列，利用1st—TID对支持度进行计数．实验表明，对于大规模事务数据库，该算法有效地提高了挖掘效率．  

An optimized sequential pattern matching methodology for sequence classification

In this paper we present a novel methodology for sequence classification, based on sequential pattern mining and optimization algorithms. The proposed methodology automatically generates a sequence classification model, based on a two stage process. In the first stage, a sequential pattern mining algorithm is applied to a set of sequences and the sequential patterns are extracted. Then, the score of every pattern with respect to each sequence is calculated using a scoring function and the score of each class under consideration is estimated by summing the specific pattern scores. Each score is updated, multiplied by a weight and the output of the first stage is the classification confusion matrix of the sequences. In the second stage an optimization technique, aims to finding a set of weights which minimize an objective function, defined using the classification confusion matrix. The set of the extracted sequential patterns and the optimal weights of the classes comprise the sequence classification model. Extensive evaluation of the methodology was carried out in the protein classification domain, by varying the number of training and test sequences, the number of patterns and the number of classes. The methodology is compared with other similar sequence classification approaches. The proposed methodology exhibits several advantages, such as automated weight assignment to classes using optimization techniques and knowledge discovery in the domain of application.

序列模式挖掘在网络业务流分析中的应用

网络业务流分析是为了适应网络优化的需要而出现的分析方法。把一种新的序列模式挖掘算法用于网络业务流分析，对网络业务的模式进行挖掘，性能上优于以往的算法。  

ADVICE: A virtual environment for Engineering Change Management

In this paper, we analyze Engineering Changes (EC), which are modifications in forms, fits, functions, materials, or dimensions in components constituting the design. We propose a new approach for processing these changes within a Virtual Collaborative Design Environment. This environment, named Active Distributed Virtual Change Environment (ADVICE) offers an Engineering Change Management solution by merging graphical and parametric data involved in the process into a virtual platform, which improves the comprehension of users and hence decreases the time required for review. ADVICE provides smart user support for predicting Engineering Changes to be triggered due to a specific change and for offering priorities to Engineering Change Requests. This is managed by employing Sequential Pattern Mining techniques to process captured Engineering Change history with Prioritization and Change Propagation mechanisms. For verifying these mechanisms, experiments involving synthetic data are conducted. The experiments present the capability of ADVICE to facilitate Engineering Change Management.  

用户访问兴趣路径挖掘方法

针对当前挖掘用户访问模式算法仅将频繁访问路径作为用户浏览兴趣路径的问题,依据使用Web日志挖掘用户兴趣页面时,通过引入页面信息量参数,综合考虑页面访问次数、浏览时间和页面信息量大小来定义用户兴趣度,提出了基于兴趣度的用户访问模式挖掘算法。实验证明该算法是有效的,在用户浏览兴趣度量方面比当前的频繁访问路径挖掘算法更准确。  

序列模式挖掘研究

为了更好地分析购物篮数据,挖掘出潜在客户,序列模式挖掘应运而生。序列模式挖掘是数据挖掘一个重要研究内容,近年来在很多领域得到广泛运用。概述序列模式挖掘的发展现状,研究基本挖掘框架的经典挖掘算法与扩展模型挖掘算法,特别针对近年来出现的新数据形式序列模式挖掘,以及基于零压缩二叉决策图(ZBDD)结构的挖掘算法做了阐述,最后对序列模式挖掘发展趋势进行了展望。  

用户行为模式挖掘问题的研究

在软件可用性测试中,分析用户行为模式是一个关键的问题。为解决具有序列长度长、以序列片断为支持度计算依据等特点的用户行为模式挖掘问题,提出了一种有效的基于前缀树的频繁事件序列扩展方法,给出了比特图索引表的构造、事件扩展、事务扩展以及支持度计算的算法。使频繁事件序列能够简单快速地被确定。  

XML文档相似性的仿真研究

XML文档相似性的计算是XML文档分类中的一个难题。文中描述了一种基于结构的方法，通过序列化模式挖掘方法，挖掘出两个文档之间的最大相似路径，从而可以通过计算最大相似的路径的节点数目和所有路径的节点数目的比值，得到两个文档之间的相似度。文章提出了一种新的最小化XML文档的方法，并且综合考虑了文档节点的语义相似度和结构相似度，从而进一步地提高了计算文档相似度的精度。实验表明，该方法有着良好的应用前景。