基于变迁图编辑距离的流程相似性算法

为了提高从企业模型库中查询检索模型的效率,提出一种基于变迁图编辑距离的流程相似性算法。首先,给出了变迁图的概念及其生成方法;其次,提出边的长度概念,且删除和插入边的代价由该边的长度决定,基于此定义出图编辑操作及其代价,并用节点匹配算法计算最小图编辑距离;然后,给出两个过程模型的相似性概念和计算方法;最后,通过实验验证了算法的正确性且满足七条相似性性质,并验证了变迁图编辑距离满足四条距离性质。     

高效低索引的图相似性搜索算法

图相似性搜索是在给定的度量标准下查找与查询图相似的图集合,目前大多采用“过滤-验证”的计算框架。针对现有方法中过滤下界不紧密和索引空间占用较大等问题,提出了一种基于查询图分区的多层级过滤、低索引空间占用的图相似性搜索算法Z-Index。该算法首先通过全局粗粒度过滤得到预候选集;然后提出基于扩展概率的查询图分区算法,并采用层级过滤机制进一步精简候选集,增强下界紧密性;最后引入序列相似性差值计算序列中数据分布的稀疏度,提出分区压缩和差值压缩两种编码压缩算法,并据此构建“零”索引结构,降低索引空间开销。实验结果表明,Z-Index算法所得下界更加紧密,产生的候选集大小可减少50%左右,算法执行时间大大缩短,且该算法在索引空间占用极小的情况下仍具有可扩展性。     

面向近邻搜索的马尔科夫图哈希算法

基于哈希编码的算法,由于其高效性,已经成为海量数据高维特征最近邻搜索的研究热点。目前存在的普遍问题是,当哈希编码长度较低时,原始特征信息保留不是很充分,从而导致检索结果不理想。为了解决这一问题,提出了一种基于Markov网络的有效哈希编码算法。该算法首先根据稀疏编码策略进行特征重构,通过Markov随机游走的方式构建特征之间的语义网络关系图,然后根据Laplacian特征映射求出投影函数,最后进行快速的线性投影二值化编码。在公开数据集上与主流算法进行了性能比较,实验结果表明该算法具备良好的检索性能。     

基于相似性搜索的水利普查数据融合

针对第一次全国水利普查数据融合存在的问题,提出自适应编辑距离相似性度量,通过调整编辑操作权重及启发式学习权重等措施,对传统的编辑距离进行改进,提高相似性搜索的准确性,并给出基于编辑距离的水利普查数据融合的方法和流程,算法的有效性在第一次全国水利普查数据处理中得到验证。     

基于模式距离度量的时间序列相似性搜索

时间序列的相似性搜索是时间序列知识发现的重要方面。该文提出了一种新的基于距离度量的时间序列相似性搜索算法。该算法采用分段线性表示，同时使用改进的模式距离来度量序列间的距离。     

图编辑距离概述

图编辑距离是图模式匹配技术中常用的方法之一。基于图编辑距离的匹配方法能够处理多种类型的图数据,因而受到了学术界的广泛关注。首先介绍了图编辑距离的相关概念;然后简述了基于启发式搜索技术的精确图编辑距离算法,重点分析了基于二分图匹配的近似图编辑距离算法;最后对现存的一些图编辑问题进行了总结,并对未来的发展趋势进行了展望。     

时间序列的夹角距离及相似性搜索

提出一种面向相似性搜索的时间序列近似表示和度量方法.在自适应分段线性表示的基础上,使用相邻线段间的夹角构成的角度序列近似表示时间序列,并给出夹角距离度量方法的概念和基本性质的证明过程.序列的夹角距离克服了用点距离度量相似性时鲁棒性差以及物理概念不明确等缺陷,而且具有平移和旋转不变性的突出优点.对人工数据和实际股票数据进行相似搜索,实验结果证明该方法的有效性.     

基于参考集索引的高效序列相似性查找算法

序列数据在文本、Web访问日志文件、生物数据库中普遍存在,对其进行相似性查找是一种重要的获取和分析知识的手段.基于参考集索引技术是一类解决序列相似性查找的有效方法,主要思想是找到序列数据库中的少数序列作为参考集,通过参考集过滤掉数据库中与查询序列不相关的数据,从而高效地回答查询.在现有基于参考集索引技术的基础上,提出一种过滤能力更强的序列相似性查询算法IRI(improved reference indexing).首先,充分利用了先前的查询结果集来加速当前的查询,其次考虑了基于序列特征的上界和下界,使得应用参考集进行过滤的上下界更紧,过滤能力进一步加强.最后,为了避免候选集中费时的编辑距离计算,则只计算前缀序列间的编辑距离,从而进一步加速算法运行.实验采用真实的DNA序列和蛋白质序列数据,结果表明,算法IRI在查询性能上明显优于现有的基于参考集索引方法RI(reference indexing).     

基于树编辑距离的层次聚类算法

为了识别犯罪嫌疑人伪造和篡改的虚假身份,利用树编辑距离计算个体属性相似性,证明了树编辑距离的相关数学性质,对属性应用层次编码方法,提出了一种新的基于树编辑距离的层次聚类算法HCTED(Hi-erarchical Clustering Algorithm Based on Tree Edit Distance)。新算法通过树编辑操作使用最少的代价计算属性相似性,克服了传统聚类算法标称型计算的缺陷,提高了聚类精度,通过设定阈值对给定样本聚类。实验证明了新方法在身份识别上的准确性和有效性,讨论了不同参数对实验结果的影响,对比传统聚类算法,HCTED算法性能明显提高。新算法已经应用到警用流动人口分析中,取得了良好效果。     

图数据库中的相似性搜索算法研究与应用*

图数据库的相似性搜索是一个非常重要的研究内容,图的相似性匹配属于图同构的判定问题,是NP完全问题,传统的高开销搜索的方法已经不能满足复杂图查询的需要;另外,由于图数据库的复杂性和特殊性,已有的优化算法不能直接使用。为了提高图数据库的搜索效率,提出了一种基于索引的相似性搜索算法,通过数据库中的频繁结构建立特征索引,算法可高效准确地滤除大量的非相似图集合,避免了图之间精确匹配即图同构的计算,最后将本算法应用于化学数据库,实验结果证明了该方法的有效性和可行性。     

A novel edge-centric approach for graph edit similarity computation

Graph similarity is an important notion with many applications. Graph edit distance is one of the most flexible graph similarity measures available. The main problem with this measure is that in practice it can only be computed for small graphs due to its exponential time complexity. This paper addresses the high complexity of graph edit distance computations. Specifically, we present CSI_GED, a novel edge-centric approach for computing graph edit distance through common sub-structure isomorphisms enumeration. CSI_GED utilizes backtracking search combined with a number of heuristics to reduce memory requirements and quickly prune away a large portion of the mapping search space. Experiments show that CSI_GED is highly efficient for computing graph edit distance; it outperforms the state-of-the-art methods by over three orders of magnitude. It also shows that CSI_GED scales the computation gracefully to larger and distant graphs on which current methods fail to run. Moreover, we evaluated CSI_GED as a stand-alone graph edit similarity search query method. The experiments show that CSI_GED is effective and scalable, and outperforms the state-of-the-art indexing-based methods by over two orders of magnitude.     

基于编辑图的XML文档相似性研究

目前关于XML文档相似性算法有很多种,其中基于编辑距离的方法是很重要的一类。目前已发表的基于编辑距离的算法中,编辑图算法由于其计算高效率的特点成为研究的出发点。首先介绍了编辑图算法的思想,由于它在计算过程中对同层兄弟节点的顺序有很强的依赖性,因此不能准确有效地比较数据无序的数据中心的XML文档相似性。针对该问题,在编辑图算法思想的基础上,结合路径算法的思想提出拆分编辑图算法。实验结果表明,拆分编辑图算法降低了编辑图算法中对兄弟节点次序的依赖性,更适合于数据中心的XML文档相似性比较,而且所得结果更加准确有效。     

Automatic learning of cost functions for graph edit distance

Graph matching and graph edit distance have become important tools in structural pattern recognition. The graph edit distance concept allows us to measure the structural similarity of attributed graphs in an error-tolerant way. The key idea is to model graph variations by structural distortion operations. As one of its main constraints, however, the edit distance requires the adequate definition of edit cost functions, which eventually determine which graphs are considered similar. In the past, these cost functions were usually defined in a manual fashion, which is highly prone to errors. The present paper proposes a method to automatically learn cost functions from a labeled sample set of graphs. To this end, we formulate the graph edit process in a stochastic context and perform a maximum likelihood parameter estimation of the distribution of edit operations. The underlying distortion model is learned using an Expectation Maximization algorithm. From this model we finally derive the desired cost functions. In a series of experiments we demonstrate the learning effect of the proposed method and provide a performance comparison to other models.     

基于树编辑距离的工作流距离度量方法

在工作流的发现和聚类等应用中,需要对两个工作流模型的距离进行度量。因此,提出一种计算两个不同结构化工作流的距离定量度量方法。首先介绍了结构化工作流,并将每一个结构化工作流转换为流程结构树;然后基于两个结构树之间的树编辑距离来计算工作流之间的距离及相应相似度。该距离度量方法满足距离度量的3个属性,即同实体不可区分性、对称性和三角不等式性质。这些属性使得该距离度量方法可以在工作流模型管理活动中作为定量分析工具。实验结果表明,基于树编辑距离的工作流度量方法是可行的。同时,与基于邻接矩阵的距离度量方法相比,该方法考虑了不同结构之间的语义距离,有效验证了此方法的合理性。     

Tree edit distance: Robust and memory-efficient

Hierarchical data are often modelled as trees. An interesting query identifies pairs of similar trees. The standard approach to tree similarity is the tree edit distance, which has successfully been applied in a wide range of applications. In terms of runtime, the state-of-the-art algorithm for the tree edit distance is RTED, which is guaranteed to be fast independent of the tree shape. Unfortunately, this algorithm requires up to twice the memory of its competitors. The memory is quadratic in the tree size and is a bottleneck for the tree edit distance computation.In this paper we present a new, memory efficient algorithm for the tree edit distance, AP-TED (All Path Tree Edit Distance). Our algorithm runs at least as fast as RTED without trading in memory efficiency. This is achieved by releasing memory early during the first step of the algorithm, which computes a decomposition strategy for the actual distance computation. We show the correctness of our approach and prove an upper bound for the memory usage. The strategy computed by AP-TED is optimal in the class of all-path strategies, which subsumes the class of LRH strategies used in RTED. We further present the AP-TED⁺ algorithm, which requires less computational effort for very small subtrees and improves the runtime of the distance computation. Our experimental evaluation confirms the low memory requirements and the runtime efficiency of our approach.     

Approximation of graph edit distance based on Hausdorff matching

Graph edit distance is a powerful and flexible method for error-tolerant graph matching. Yet it can only be calculated for small graphs in practice due to its exponential time complexity when considering unconstrained graphs. In this paper we propose a quadratic time approximation of graph edit distance based on Hausdorff matching. In a series of experiments we analyze the performance of the proposed Hausdorff edit distance in the context of graph classification and compare it with a cubic time algorithm based on the assignment problem. Investigated applications include nearest neighbor classification of graphs representing letter drawings, fingerprints, and molecular compounds as well as hidden Markov model classification of vector space embedded graphs representing handwriting. In many cases, a substantial speedup is achieved with only a minor loss in accuracy or, in one case, even with a gain in accuracy. Overall, the proposed Hausdorff edit distance shows a promising potential in terms of flexibility, efficiency, and accuracy.     

一种基于文档拓扑的相似性搜索算法

从海量文档中快速有效地搜索到相似文档是一个重要且耗时的问题。现有的文档相似性搜索算法是先找出候选文档集,再对候选文档进行相关性排序,找出最相关的文档。提出了一种基于文档拓扑的相似性搜索算法——Hub-N,将文档相似性搜索问题转化为图搜索问题,应用相应的剪枝技术,缩小了扫描文档的范围,提高了搜索效率。通过实验验证了算法的有效性和可行性。