频繁模式挖掘的研究对象包括事务、序列、树和图。
Frequent patterns mining involves mining transactions, sequences, trees and graphs.
在频繁模式挖掘过程中能够动态改变约束的算法比较少。
A new algorithm, constrain-based frequent patterns mining, was developed to provide frequent pattern mining with constraints.
为了解决这一问题，提出了一种多关系频繁模式挖掘算法。
In order to solve this problem, this paper proposed a multi-relational frequent pattern mining algorithm.
上述工作可以为频繁模式挖掘及关联规则的研究提供有益的参考。
The above work can give a valuable reference for frequent pattern mining and association rules studying.
基于序贯频繁模式挖掘，提出并实现了一种宏观网络流量异常检测的方法。
This paper presents and implements a macro-network traffic anomaly detection strategy based on sequential frequent pattern mining.
在此基础上，进一步介绍了频繁模式挖掘的经典算法以及电信收入保障系统的体系结构。
On this basis, further information on the classic frequent pattern mining algorithm and income security systems of telecommunications architecture is introduced.
频繁模式挖掘是数据挖掘领域的一个重要方面，研究内容一般包括事务、序列、树和图。
Frequent patterns mining is an important aspect of data mining and includes mining transaction, sequence, tree and graph.
频繁模式挖掘是最基本的数据挖掘问题，由于内在复杂性，提高挖掘算法性能一直是个难题。
Frequent pattern mining is a fundamental data mining problem for which algorithms still suffer from inefficiencies because of the inherent complexities.
本文以标记有序树作为半结构化数据的数据模型，研究了半结构化数据的树状最大频繁模式挖掘问题。
In this paper, labeled ordered tree is used as the data model of semi structured data, the problem of maximum tree structured frequent pattern mining from semi structured data is studied.
分析结果表明，利用规则兴趣度能够大大减小候选项目集的大小，有效提高频繁模式挖掘算法的效率。
The result indicates that we can remarkably decrease the candidate items and improve the efficiency of mining frequent pattern when using the interest measure.
提出了一种可直接用于快速频繁模式挖掘的频繁项目表的概念，并实现了具体的频繁模式增量挖掘方法。
Based on a new idea of frequent item table which can be directly used in fast frequent mode mining, an effective FP_growth mining algorithm is presented in this paper.
该方法主要是针对频繁模式的挖掘，现有的频繁模式挖掘的模型各有优点，但在智能性和通用性方面表现的较差。
The technique focus on frequent pattern mining, the existed models of frequent pattern mining have the advances respectively, but there is shortcoming in the intelligence and universality.
在论述数据流管理系统模型的基础上，深入分析了国内外的各种频繁模式挖掘算法，并指出这些算法的特点及其局限性。
Based on comment of DSMS model, various frequent pattern mining algorithms are analyzed thoroughly and their characteristics and limitation are pointed out in this paper.
提高序列模式挖掘算法效率的关键在于减少发现频繁序列的时间。
To speed up mining sequential patterns, reducing the time cost is very important during discovering sequential frequent sequence.
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。
An active research in data mining area is the discovery of sequential patterns, which finds all frequent sub-sequences in a sequence database.
因此，文章提出了一种最大频繁模式的快速挖掘算法DMFP及更新算法IUMFP。
This paper proposes a fast algorithm DMFP and an updating algorithm IUMFP, which are based on Prefix Tree for mining maximum frequent patterns.
序列模式挖掘就是发现序列数据库中的频繁子序列作为用户感兴趣的模式。
Sequential pattern mining, which discovers frequent subsequences as interesting patterns in a sequence database.
本文对RNA分子建立树形模型，利用频繁子树挖掘算法挖掘RNA二级结构中的公共拓扑模式。
The paper builds tree-model of RNA molecules and utilizes frequent sub tree mining algorithm to mine common topological patterns among RNA secondary structures.
随着对大量结构化数据分析需求的增长，从图集合中挖掘频繁子图模式已经成为数据挖掘领域的研究热点。
With the increasing demand of massive structured data analysis, mining frequent subgraph patterns from graph datasets has been an attention-deserving field.
国内外学者已提出许多新的挖掘数据流频繁模式的方法和技术。
Many new techniques and methods on frequent pattern mining in data stream have been proposed.
提出了一个新的数据库存储结构AF P -树，利用它来挖掘频繁模式。然后利用项目之间的相互关联做出推荐。最后举例说明了此推荐系统的处理过程。
It proposes a new database store structure AFP-Tree for mining frequent patterns, makes recommendations by exploring associations between items, exemplifies the approach on real data.
数据挖掘领域的一个活跃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。
An active research in data mining area is the discovery of sequential patterns, which finds all frequent sub - sequences in a sequence database.
摘要：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。
Absrtact: An active research in data mining area is the discovery of sequential patterns, which finds all frequent sub - sequences in a sequence database.
首先对有代表性的挖掘算法从算法思想、关键技术、算法的优缺点进行了分析概括，此后列举了一些典型频繁模式及关联规则的领域应用。
After analyzing and summarizing on ideas, key technology, advantage and disadvantage for some representative frequent pattern mining algorithms have been done, the typical application is listed.
挖掘和更新最大频繁模式是多种数据挖掘应用中的关键问题。
Mining and updating maximum frequent patterns is a key problem in data mining research.
如何确定候选频繁序列模式以及如何计算它们的支持数是序列模式挖掘中的两个关键问题。
How to generate candidate frequent sequential pattern and calculate its support is a key problem in mining frequent sequential patterns.
频繁闭合模式挖掘是关联分析的关键步骤。
The mining of frequent closed patterns plays an essential role in association analysis method.
挖掘事务数据库、时间序列数据库中的频繁模式已经成为数据挖掘中很受关注的研究方向。
Mining frequent patterns in transaction databases, time series databases, and many other kinds of databases has been studied popularly in data mining research.
挖掘事务数据库、时间序列数据库中的频繁模式已经成为数据挖掘中很受关注的研究方向。
Mining frequent patterns in transaction databases, time series databases, and many other kinds of databases has been studied popularly in data mining research.
应用推荐