A methodology for knowledge discovery to support product family design

This paper introduces a methodology for knowledge discovery related to product family design that integrates an ontology with data mining techniques. In the proposed methodology, the ontology represents attributes for the components of products in functional hierarchies. Fuzzy clustering is employed for data mining to first partition product functions into subsets for identifying modules in a given product family and then identify the similarity level of components in a module. Module categorization is introduced to support association rule mining for knowledge discovery related to platform design. We apply the proposed methodology to first develop and then utilize design knowledge for a family of power tools. Based on the developed design knowledge, a new platform is suggested to improve commonality in the power tool family.     

Using DEA and survival analysis for measuring performance of branches in New Zealand's Accident Compensation Corporation

This paper describes an approach to solving the problem of assessing relative branch performance in the Accident Compensation Corporation (ACC), the New Zealand state-owned, no-fault, personal injury compensation insurance company. The methodology described in this paper is innovative in that it assesses relative performance via a modified data envelopment analysis (DEA) process. Performance is assessed and can be examined in terms that are meaningful to business goals. Performance evaluation has generally aligned well with management preconceptions of performance. DEA results are clustered to find common performance themes that are reviewed for business acceptability and the successful themes are used to measure performance against a Farrell frontier. The methodology is applicable to other situations where there are multiple instances of a unit performing the same or similar functions. Technical analysis as used in this paper is based on survival analysis, DEA, clustering and knowledge of the organisation's business operations and objectives.     

Combining data mining and case-based reasoning for intelligent decision support for pathology ordering by general practitioners

Pathology ordering by general practitioners (GPs) is a significant contributor to rising health care costs both in Australia and worldwide. A thorough understanding of the nature and patterns of pathology utilization is an essential requirement for effective decision support for pathology ordering. In this paper a novel methodology for integrating data mining and case-based reasoning for decision support for pathology ordering is proposed. It is demonstrated how this methodology can facilitate intelligent decision support that is both patient-oriented and deeply rooted in practical peer-group evidence. Comprehensive data collected by professional pathology companies provide a system-wide profile of patient-specific pathology requests by various GPs as opposed to that limited to an individual GP practice. Using the real data provided by XYZ Pathology Company in Australia that contain more than 1.5 million records of pathology requests by general practitioners (GPs), we illustrate how knowledge extracted from these data through data mining with Kohonen’s self-organizing maps constitutes the base that, with further assistance of modern data visualization tools and on-line processing interfaces, can provide “peer-group consensus” evidence support for solving new cases of pathology test ordering problem. The conclusion is that the formal methodology that integrates case-based reasoning principles which are inherently close to GPs’ daily practice, and data-driven computationally intensive knowledge discovery mechanisms which can be applied to massive amounts of the pathology requests data routinely available at professional pathology companies, can facilitate more informed evidential decision making by doctors in the area of pathology ordering.     

An analysis of customer retention and insurance claim patterns using data mining: a case study

The insurance industry is concerned with many problems of interest to the operational research community. This paper presents a case study involving two such problems and solves them using a variety of techniques within the methodology of data mining. The first of these problems is the understanding of customer retention patterns by classifying policy holders as likely to renew or terminate their policies. The second is better understanding claim patterns, and identifying types of policy holders who are more at risk. Each of these problems impacts on the decisions relating to premium pricing, which directly affects profitability. A data mining methodology is used which views the knowledge discovery process within an holistic framework utilising hypothesis testing, statistics, clustering, decision trees, and neural networks at various stages. The impacts of the case study on the insurance company are discussed.     

一种新的股市风险度量指标及其应用

本文充分利用了多元统计分析的技术 ,提出了用收益的方差协方差矩阵引出的特征根作为股市风险的一种量度指标 ,在此基础上 ,推导出了第一特征向量的分块结构公式 ,并给出了具有实际意义的解释。本文的思想及技术路线 ,为以后对股市风险定量研究及开发金融工程产品打开了新的研究视野     

The Circle of Apollonius: A Discovery Activity

The works of the Greek mathematician Apollonius offer many opportunities for conjecture regarding the discovery of geometric relationships. The “circle of Apollonius” can provide students a chance to use simple constructions and knowledge of proportions to discover an unexpected result, and, if desired, a chance to follow that discovery with a proof that what appears to be so is, indeed, the case. A minimum of prior knowledge is needed for the conjecturel discovery phase of this activity, while the proof requires a more solid background of Euclidean geometry. This exercise can lead to a number of other relationships and constructions found in the works of Apollonius.     

A note on knowledge discovery using neural networks and its application to credit card screening

We address an important issue in knowledge discovery using neural networks that has been left out in a recent article “Knowledge discovery using a neural network simultaneous optimization algorithm on a real world classification problem” by Sexton et al. [R.S. Sexton, S. McMurtrey, D.J. Cleavenger, Knowledge discovery using a neural network simultaneous optimization algorithm on a real world classification problem, European Journal of Operational Research 168 (2006) 1009–1018]. This important issue is the generation of comprehensible rule sets from trained neural networks. In this note, we present our neural network rule extraction algorithm that is very effective in discovering knowledge embedded in a neural network. This algorithm is particularly appropriate in applications where comprehensibility as well as accuracy are required. For the same data sets used by Sexton et al. our algorithm produces accurate rule sets that are concise and comprehensible, and hence helps validate the claim that neural networks could be viable alternatives to other data mining tools for knowledge discovery.     

Consensus of heterogeneous multi-agent systems with linear and nonlinear dynamics

In this paper, we perform an in-depth study about the consensus problem of heterogeneous multi-agent systems with linear and nonlinear dynamics.Specifically, this system is composed of two classes of agents respectively described by linear and nonlinear dynamics. By the aid of the adaptive method and Lyapunov stability theory, the mean consensus problem is realized in the framework of first-order case and second-order case under undirected and connected networks.Still, an meaningful example is provided to verify the effectiveness of the gained theoretical results. Our study is expected to establish a more realistic model and provide a better understanding of consensus problem in the multi-agent system.     

Estimating the demand pattern for C category items

This paper presents a methodology for estimating the demand pattern for the slowest-moving C category inventory items. The methodology uses an aggregation-by-items scheme and a forecasting procedure based on conditional demand analysis whereby aggregate demand is assumed to be an arbitrarily mixed, heterogeneous Poisson distribution. Practical aspects of demand heterogeneity, parameter estimation and model implementation are illustrated using a case study in retail inventory planning and control.     

An Expert System Framework for Economic Evaluation of Machining Operation Planning

Recognising the importance of combining manufacturing and management systems for machining operation planning, this paper presents a new methodology for the evaluation of economic aspects in an operation plan. To ensure that the quality of machined parts satisfies the required specifications, the manufacturing system acts as an alternative generator that provides meaningful and practical plans. Through cost analysis, the variable, fixed, and total costs associated with the machining operation are quantitatively determined. The management system, which functions as an evaluation mechanism, then selects the optimal plan based on the defined goal. The proposed methodology has been applied in the framework design of an expert system. The program establishes a sequence of machining operation planning and searches for the optimal plan. This optimal plan integrates considerations from both managers and production engineers, and balances their needs for efficient machining of a quality product.     

Reliability inference with extended sequential order statistics

In this article, we will address the complexity of non-identical components in multi-component systems. Most technical systems can be described as such since either component types or component functions within the system vary amongst components. While most reliability related work resorts to the assumption of homogeneous components, we aim to address the often more realistic assumption of heterogeneous components extending the model of Extended Sequential Order Statistics by two inferential methods. Firstly, the derivation of Maximum Likelihood Estimates including a simulation study demonstrating their good performance for large enough sample size. Secondly, we introduce a likelihood ratio test to test whether components can be assumed identical accompanied by a power study. Both methods are powerful tools in reliability contexts. The former increases our understanding of component behaviour, especially upon failure of other components. This knowledge empowers system operators to make better decisions regarding maintenance schedules and failure time prediction. The latter supports operators in their quest of identifying component equivalence. Therefore, both methods can be used to achieve meaningful results in real life applications.     

Stochastic optimization on social networks with application to service pricing

In this paper we develop a combined simulation and optimization approach for solving difficult decision problems on complex dynamic networks. For a specific reference problem we consider a telecommunication service provider who offers a telecommunication service to a market with network effects. More particularly, the service consumption of an individual user depends on both idiosyncratic characteristics and the popularity of this service among the customer’s immediate neighborhood. Both the social network and the individual user preferences are largely heterogeneous and changing over time. In addition the service provider’s decisions are made in absence of perfect knowledge about user preferences. The service provider pursues the strategy of stimulating the demand by offering differentiated prices to the customers. For finding the optimal pricing we apply a stochastic quasi-gradient algorithm that is integrated with a simulation model that drives the evolution of the network and user preferences over time. We show that exploiting the social network structure and implementing differentiated pricing can substantially increase the revenues of a service provider operating on a social network. More generally, we show that stochastic gradient methods represent a powerful methodology for the optimization of decisions in social networks.     

Knowledge Extraction from Aerodynamic Design Data and its Application to 3D Turbine Blade Geometries

Applying numerical optimisation methods in the field of aerodynamic design optimisation normally leads to a huge amount of heterogeneous design data. While most often only the most promising results are investigated and used to drive further optimisations, general methods for investigating the entire design dataset are rare. We propose methods that allow the extraction of comprehensible knowledge from aerodynamic design data represented by discrete unstructured surface meshes. The knowledge is prepared in a way that is usable for guiding further computational as well as manual design and optimisation processes. A displacement measure is suggested in order to investigate local differences between designs. This measure provides information on the amount and direction of surface modifications. Using the displacement data in conjunction with statistical methods or data mining techniques provides meaningful knowledge from the dataset at hand. The theoretical concepts have been applied to a data set of 3D turbine stator blade geometries. The results have been verified by means of modifying the turbine blade geometry using direct manipulation of free form deformation (DMFFD) techniques. The performance of the deformed blade design has been calculated by running computational fluid dynamic (CFD) simulations. It is shown that the suggested framework provides reasonable results which can directly be transformed into design modifications in order to guide the design process.     

A social-event based approach to sentiment analysis of identities and behaviors in text

We describe a new methodology to infer sentiments held toward identities and behaviors from social events that we extract from a large corpus of newspaper text. Our approach draws on affect control theory, a mathematical model of how sentiment is encoded in social events and culturally shared views toward identities and behaviors. While most sentiment analysis approaches evaluate concepts on a single, evaluative dimension, our work extracts a three-dimensional sentiment “profile” for each concept. We can also infer when multiple sentiment profiles for a concept are likely to exist. We provide a case study of a large newspaper corpus on the Arab Spring, which helps to validate our approach.     

Knowledge structure,knowledge granulation and knowledge distance in a knowledge base

One of the strengths of rough set theory is the fact that an unknown target concept can be approximately characterized by existing knowledge structures in a knowledge base. Knowledge structures in knowledge bases have two categories: complete and incomplete. In this paper, through uniformly expressing these two kinds of knowledge structures, we first address four operators on a knowledge base, which are adequate for generating new knowledge structures through using known knowledge structures. Then, an axiom definition of knowledge granulation in knowledge bases is presented, under which some existing knowledge granulations become its special forms. Finally, we introduce the concept of a knowledge distance for calculating the difference between two knowledge structures in the same knowledge base. Noting that the knowledge distance satisfies the three properties of a distance space on all knowledge structures induced by a given universe. These results will be very helpful for knowledge discovery from knowledge bases and significant for establishing a framework of granular computing in knowledge bases.     

一种新型的广义规则推理算法

数据挖掘是指从大型数据库的海量信息中有效进行知识发现的过程,而其效能的高低主要取决于搜索机制所依据的算法.有鉴于此,提出了一种基于个体免疫与群体进化机制于一体的一种高效的全局优化搜索算法,即基于免疫规划的广义规则推理算法.与已有算法所不同的是,广义规则推理算法不仅仅着眼于发现一些有关分类方面的信息,而是利用背景理论和先验知识在知识表示与运行效率之间相均衡的基础上,着重新知识的发现和对高级规则的预测.理论分析和仿真实验表明,广义规则推理算法有利于进化群体的相对稳定和整体性能的提高,并可以在规则提取过程中保持较高的精确度.     

Tech Prep Academics: Using Real Life Connections to Develop Scientific and Mathematical Literacy

Based upon the recommendations of professional organizations in science and mathematics education, children at K-6 levels need to be exposed to activities involving scientific methodology, the discovery of new knowledge and the integration of science and mathematics curricula. This study describes several distinct kinds of problem solving investigations identified from real life situations which can be adapted in intellectually honest ways for selected levels of the elementary school curriculum. The activities lend themselves to interactions with businesses and industries in the children's community and involve the children in a variety of non-traditional instructional activities such as oral presentations, small group collaborative efforts, and written reports. Finally, the investigations promote the integration of science and mathematics curricula and suggest the role curricula can play in the lives of children.     

Fuzzy目标信息系统的知识发现

本文在Pawlak提出的Rough集的知识发现的基础上,给出了Fuzzy目标信息系统的知识发现和知识约简方法。     

Customer satisfaction measurement in the private bank sector

Customer satisfaction represents a modern approach for quality in enterprises and organisations and serves the development of a truly customer-focused management and culture. Measuring customer satisfaction offers an immediate, meaningful and objective feedback about clients’ preferences and expectations. In this way, company’s performance may be evaluated in relation to a set of satisfaction dimensions that indicate the strong and the weak points of a business organisation. This paper presents an original customer satisfaction survey in the private bank sector. The implemented methodology is based on the principles of multicriteria analysis and preference disaggregation modelling. The most important results are focused on the determination of the critical service dimensions and the segmentation to customer clusters with distinctive preferences and expectations.