A Further Comparison of Splitting Rules for Decision-Tree Induction

One approach to learning classification rules from examples is to build decision trees. A review and comparison paper by Mingers (Mingers, 1989) looked at the first stage of tree building, which uses a splitting rule to grow trees with a greedy recursive partitioning algorithm. That paper considered a number of different measures and experimentally examined their behavior on four domains. The main conclusion was that a random splitting rule does not significantly decrease classificational accuracy. This note suggests an alternative experimental method and presents additional results on further domains. Our results indicate that random splitting leads to increased error. These results are at variance with those presented by Mingers.     

数据仓库技术在零售业税务决策支持系统中的应用

网络税控系统用于监管零售行业的税收,它从零售企业的MIS和POS系统中采集税控信息,实现了销售小票与机打发票的统一,并采用传统的关系数据库来存储收集的税控数据。本文阐述了在网络税控系统的基础上应用数据仓库技术构建零售业税务决策支持系统的框架方案,并重点介绍了零售业税务决策支持系统数据仓库总体架构以及数据仓库的设计实现过程。本文作者创新点:将数据仓库技术应用到零售业税务决策支持系统中,为税务部门提供了决策支持。     

数据挖掘在电子商务中的应用研究

本文主要介绍数据挖掘在电子商务中的应用,文章从数据挖掘的定义、数据挖掘的常用方法出发,阐述了数据挖掘在海量信息中提取有效信息的作用,特别是数据挖掘与电子商务的结合可以给人们生活带来便利,给企业带来新的利润空间以及为企业提供决策支持。常用的数据挖掘技术主要包括支持向量机、朴素贝叶斯、聚类、决策树、神经网络和关联规则等,最后介绍了数据挖掘在电子商务的4个主要应用方面,说明了数据挖掘存在的必要性。     

高校餐饮服务信息化综合解决方案

高校餐饮服务相比较于社会化餐饮服务具有其独特特性,在购买原材料完全遵循市场规则而销售定价不能完全市场化的限制下,还要面对窗口自选以及餐厅点餐两种销售模式并存的影响,以及伴随着扩招带来了多校区、多食堂、多窗口的状况,如何通过信息化手段提高其工作流程和效率,成为当前迫切需要。文中提出一种符合实际工作需要的信息化解决方案,对整个高校餐饮服务过程中所涉及的人、物、财进行综合管理,并形成决策,以最大化信息系统的效用,真正做到开源节流。     

改进的Dijkstra算法在灾害决策系统中的应用

灾害决策系统中,核心安置点的选择对救灾工作效率影响重大;分析常用的最短路径搜索方法,选用Dijkstra算法并对其进行改进,计算选取到达最远安置点最近的安置点作为核心安置点。实验结果表明,该方法具有良好的实用性。     

逻辑表达式图的实现及其在集成电路故障 可测性中的应用

数字电路的可靠性有着至关重要的影响,测试是其重要保证,测试向量的自动生成(ATPG)在数字电路的测试中占有重要地位;逻辑表达式图(Boolean Expression Diagrams,BED)是用于逻辑函数与逻辑电路表达与运算一种数据结构,能够将逻辑电路在线性空间复杂度内表达,是二元判决图(Binary Decision Diagrams, BDD)在概念上的推广且保留着BDD的许多有用的性质。讨论了BED的性质与实现方法,并将BED用于逻辑电路呆滞型故障测试向量的自动生成中,基于BED的测试算法直接将原电路与故障电路做异或运算后用BED表达再化简或判断其可满足性,算法能充分使用逻辑代数的化简规则和利用电路与故障电路的相似性。实验结果表明,基于BED的测试方法具有较低的复杂度。     

Impact of Argumentative Negotiation on Collaborative Engineering

Engineering of complex systems involves multiple disciplinary design teams with diversified skills. The team members must work together to make joint decisions, but are often faced with difficulties when trying to reach agreements. Negotiation has been studied as a method for facilitating information exchange, mutual understanding, and joint decision-making. In our previous work, we introduced an argumentative negotiation model to support collaborative engineering. In this paper, we present an experiment study that was conducted to assess the impact of this negotiation support system on the process and the outcome of collaborative design. The results of the experiment have demonstrated the positive effects of the approach.     

商榷用水煤浆代替轻油改造年产30万吨合成氨

介绍用水煤浆代替轻油原料，改造年产３０万吨合成氨装置，重点论述其制氨工艺单元的合理组合和国产化措施。     

Decision support and intelligent systems in the textile and apparel supply chain: An academic review of research articles

This article provides a comprehensive review of research articles related to the application of decision support and intelligent systems in the textile and apparel supply chains. Data were obtained from 77 articles published from 1994 to 2009 in 35 journals. The articles were categorized according to their applicability into three basic sectors – textile production, apparel manufacture, and distribution/sales. They were further categorized into 16 subsectors based on their operational and management/control processes. A comprehensive list of categorized journal articles identified in this study provides insights and relevant references for both researchers and practitioners on the application of decision support and intelligent systems to various stages of a textile and apparel supply chain. In light of the developed classification framework, we identify gaps in extending the use of the decision support and artificial intelligent systems in the industry and suggest potential and applicable research areas for further consideration in this subject area.     

Development of a Composite Knowledge Manipulation Tool: K-Expert

A goal of this study is to develop a Composite Knowledge Manipulation Tool (CKMT). Some of traditional medical activities are rely heavily on the oral transfer of knowledge, with the risk of losing important knowledge. Moreover, the activities differ according to the regions, traditions, experts’ experiences, etc. Therefore, it is necessary to develop an integrated and consistent knowledge manipulation tool. By using the tool, it will be possible to extract the tacit knowledge consistently, transform different types of knowledge into a composite knowledge base (KB), integrate disseminated and complex knowledge, and complement the lack of knowledge. For the reason above, I have developed the CKMT called as K-Expert and it has four advanced functionalities as follows. Firstly, it can extract/import logical rules from data mining (DM) with the minimum of effort. I expect that the function can complement the oral transfer of traditional knowledge. Secondly, it transforms the various types of logical rules into database (DB) tables after the syntax checking and/or transformation. In this situation, knowledge managers can refine, evaluate, and manage the huge-sized composite KB consistently with the support of the DB management systems (DBMS). Thirdly, it visualizes the transformed knowledge in the shape of decision tree (DT). With the function, the knowledge workers can evaluate the completeness of the KB and complement the lack of knowledge. Finally, it gives SQL-based backward chaining function to the knowledge users. It could reduce the inference time effectively since it is based on SQL query and searching not the sentence-by-sentence translation used in the traditional inference systems. The function will give the young researchers and their fellows in the field of knowledge management (KM) and expert systems (ES) more opportunities to follow up and validate their knowledge. Finally, I expect that the approach can present the advantages of mitigating knowledge loss and the burdens of knowledge transformation and complementation.