多数据库中例外模式挖掘方法研究*

首先比较了现有的两种挖掘方法,提出了一种改进技术.综合考虑例外的局部和全局兴趣度,剔除非真正有趣的局部例外;增加两种客观度量并按模式重要度排序.实验表明该方法不仅可以有效挖掘多数据库中例外模式,而且还大大减少了用户负担.     

基于统计相关性的兴趣关联规则的挖掘

本文首先对关联规则的支持—置信框架存在的不足进行了分析,然后引入了规则的兴趣度概念,利用兴趣度来约束冗余关联规则的产生,以提高挖掘知识的有用性,并给出了算法描述。     

ARMiner: A Data Mining Tool Based on Association Rules

In this paper,ARM iner,a data mining tool based on association rules,is introduced.Beginning with the system architecture,the characteristics and functions are discussed in details,including data transfer,concept hierarchy generalization,mining rules with negative items and the re-development of the system.An example of the tool‘s application is also shown.Finally,Some issues for future research are presented.     

正相关性指导下的关联规则剪枝算法

基于支持度和置信度模型的关联规则剪枝算法会挖掘出很多无趣规则。针对该问题,提出一种正相关性指导下的关联规则剪枝算法。利用全置信度和提升度构造一个正相关性评价函数,以此对频繁项集进行剪枝。实验结果表明,该算法能减少无趣关联规则数量,提升挖掘结果质量,缩短挖掘时间。     

基于用户兴趣度的校园网带宽管理策略应用研究

针对校园网出口拥塞问题,提出一种基于行为分析的用户兴趣建模方法,通过用户参与时间来衡量并计算不同种类应用的用户访问兴趣度,在用户兴趣度的基础上结合通道管理技术进行带宽管理策略研究,实现动态带宽管理。实施结果表明,该方法能够有效地提高用户的网络满意度。     

应对同质竞争的个性化包装设计

基于商品同质化与包装设计同质化的市场竞争,提出从包装结构设计角度考虑的个性化设计方法,并从包装结构的趣味性、舒适性、多用途来展开分析,重新认识、思考现代包装结构设计的新形态、新理念,以适应市场经济的需要。     

CARM: Context Based Association Rule Mining for Conventional Data

This paper is aimed to develop an algorithm for extracting association rules, called Context-Based Association Rule Mining algorithm (CARM), which can be regarded as an extension of the Context-Based Positive and Negative Association Rule Mining algorithm (CBPNARM). CBPNARM was developed to extract positive and negative association rules from Spatio-temporal (space-time) data only, while the proposed algorithm can be applied to both spatial and non-spatial data. The proposed algorithm is applied to the energy dataset to classify a country’s energy development by uncovering the enthralling interdependencies between the set of variables to get positive and negative associations. Many association rules related to sustainable energy development are extracted by the proposed algorithm that needs to be pruned by some pruning technique. The context, in this paper serves as a pruning measure to extract pertinent association rules from non-spatial data. Conditional Probability Increment Ratio (CPIR) is also added in the proposed algorithm that was not used in CBPNARM. The inclusion of the context variable and CPIR resulted in fewer rules and improved robustness and ease of use. Also, the extraction of a common negative frequent itemset in CARM is different from that of CBPNARM. The rules created by the proposed algorithm are more meaningful, significant, relevant and insightful. The accuracy of the proposed algorithm is compared with the Apriori, PNARM and CBPNARM algorithms. The results demonstrated enhanced accuracy, relevance and timeliness.     

浅谈生活陶艺的趣味性

生活陶艺作为一门既传统又现代的陶瓷艺术形式,从彩陶时期,一直伴随着人们的日常生活。生活陶艺在拥有实用性的同时,也很有趣味性。本文围绕生活陶艺的趣味性展开,从我国古代陶瓷趣味性的萌芽出发,重点论述如何运用趣味性元素发展我国现代生活陶艺。     

Understanding the Crucial Role of Attribute Interaction in Data Mining

This is a review paper, whose goal is tosignificantly improve our understanding of thecrucial role of attribute interaction in datamining. The main contributions of this paperare as follows. Firstly, we show that theconcept of attribute interaction has a crucialrole across different kinds of problem in datamining, such as attribute construction, copingwith small disjuncts, induction of first-orderlogic rules, detection of Simpson's paradox,and finding several types of interesting rules.Hence, a better understanding of attributeinteraction can lead to a better understandingof the relationship between these kinds ofproblems, which are usually studied separatelyfrom each other. Secondly, we draw attention tothe fact that most rule induction algorithmsare based on a greedy search which does notcope well with the problem of attributeinteraction, and point out some alternativekinds of rule discovery methods which tend tocope better with this problem. Thirdly, wediscussed several algorithms and methods fordiscovering interesting knowledge that,implicitly or explicitly, are based on theconcept of attribute interaction.     

由规则归纳系统中发掘感兴趣模式

文章借助粗糙集理论，提出一种面向大型数据库的规则归纳方法；然后在评价规则的置信度和支持度的基础上，引入兴趣度准则进一步对规则进行评价，并介绍了两种针对不同问题的兴趣度指标，提出兴趣模板的概念来描述令人感兴趣的规则特征以强化用户的参与作用，提高系统效率。