Shouhong Wang

A knowledge management approach to data mining process for business intelligence

Purpose – Data mining (DM) has been considered to be a tool of business intelligence (BI) for knowledge discovery. Recent discussions in this field state that DM does not contribute to business in a large‐scale. The purpose of this paper is to discuss the importance of business insiders in the process of knowledge development to make DM more relevant to business.Design/methodology/approach – This paper proposes a blog‐based model of knowledge sharing system to support the DM process for effective BI.Findings – Through an illustrative case study, the paper has demonstrated the usefulness of the model of knowledge sharing system for DM in the dynamic transformation of explicit and tacit knowledge for BI. DM can be an effective BI tool only when business insiders are involved and organizational knowledge sharing is implemented.Practical implications – The structure of blog‐based knowledge sharing systems for DM process can be practically applied to enterprises for BI.Originality/value – The paper suggests th...

Adaptive non-parametric efficiency frontier analysis: a neural-network-based model

There have been two schools of efficiency analysis for private and public organizations. One is the data envelopment analysis (DEA) method which is based on a mathematical programming approach, and the other is the estimation of stochastic frontier functions (SFF) which is based on the econometric regression theory. Each of these two methodologies has its strength as well as major limitations. This paper proposes a non-parametric efficiency analysis method based on the adaptive neural network technique. The proposed computational method is able to find a stochastic frontier based on a set of input-output observational data. Like SFF, the proposed method considers two types of deviations involved in input-output data: managerial (external) and observational (internal) deviations. Like DEA, the proposed method does not require explicit assumptions about the function structure of the stochastic frontier. However, unlike any SFF and stochastic DEA methods, the proposed method does not require any parametric assumption of distribution functions. Using the neural networks, this method provides an adaptive way of obtaining empirical estimates of stochastic frontiers. An example using real data is presented for illustrative purposes. Simulation experiments demonstrate that the neural-network-based method would be effective as adaptive non-parametric efficiency analysis.

Application of self-organising maps for data mining with incomplete data sets

Self-organising maps (SOM) have become a commonly-used cluster analysis technique in data mining. However, SOM are not able to process incomplete data. To build more capability of data mining for SOM, this study proposes an SOM-based fuzzy map model for data mining with incomplete data sets. Using this model, incomplete data are translated into fuzzy data, and are used to generate fuzzy observations. These fuzzy observations, along with observations without missing values, are then used to train the SOM to generate fuzzy maps. Compared with the standard SOM approach, fuzzy maps generated by the proposed method can provide more information for knowledge discovery.

Fuzzy set representation of neural network classification boundaries

In neural network classification techniques, the uncertainty of a new observation belonging to a particular class is difficult to express in statistical terms. On the other hand, statistical classification techniques are also poor for supplying uncertainty information for new observations. The use of fuzzy sets is a promising approach to providing imprecise class membership information. The monotonic function neural network is a tool that can be used to develop fuzzy membership functions. This research suggests that a multiarchitecture monotonic function neural network can be used for fuzzy set representation of classification boundaries in monotonic pattern recognition. >

Classification with incomplete survey data

Survey data are often incomplete. Classification with incomplete survey data is a new subject. This study proposes a Hopfield neural network based model of classification for incomplete survey data. Using this model, an incomplete pattern is translated into fuzzy patterns. These fuzzy patterns, along with patterns without missing values, are then used as the exemplar set for teaching the Hopfield neural network. The classifier also retains information of fuzzy class membership for each exemplar pattern. When presenting a test sample, the neural network would find an exemplar that best matches the test pattern and give the classification result. Compared with other classification techniques, the proposed method can utilize more information provided by the data with missing values, and reveal the risk of the classification result on the individual observation basis.

Knowledge management through the development of information schema

Knowledge management (KM) has been receiving considerable attention in the human-systems research community in the past few years. This paper discusses the key concepts of user-computer interaction for knowledge development and proposes a model of an information schema. Such an information repository for KM must be organized into a domain schema. Users of an information repository play an active role in searching through information to coordinate their actions. An example of information schema for KM can be found in a student advising system. This will be used to illustrate the concept of the development of an information schema.

Designing information systems for electronic commerce

Electronic commerce applies new business models. To use information technology to support a new business model, the supplier enterprise needs to develop an information system model for electronic commerce. Proposes a framework for developing an information system model to fit the business model of electronic commerce. This framework is structured around three domains: EC business model, information system model, and object‐oriented methodology.

Knowledge maps for managing Web‐based business

Knowledge management has recently received considerable attention in the Web‐based business community. This paper discusses the key concepts of human‐computer interaction in knowledge development, and identifies new challenges of knowledge management for Web‐based business. Based on theories of knowledge representations and semantic networks, this paper proposes a structure of knowledge maps for knowledge management in the Web‐based business environment. An example of knowledge maps for online auctions is used to illustrate the application of the proposed structure of knowledge maps.

Discovering patterns of missing data in survey databases: An application of rough sets

Databases for data mining often have missing values. Missing data are often mistreated in data mining and valuable knowledge related to missing data is often overlooked. This study discusses patterns of missing data in survey databases. It proposes a framework of rough set rule induction method that enables the data miner to obtain association rules of patterns of missing data in a survey database. Through an experiment on a real-world data set, we demonstrate the approach to discovering knowledge about missing data.

A multiple perspectives approach to supplier selection

Purpose – The purpose of this paper is to change the traditional supplier selection methods by shifting the emphasis from using a single model to using multiple models in the unstructured decision‐making context and to provide a tool for decision makers to make informed decisions of supplier selection in the multiple perspectives.Design/methodology/approach – There are various supplier selection models available in the literature. However, using the result of a single model as a basis for making the final decision could lead to a biased decision given the fact that any model has its limitations. The qualities of the decision‐making process and the decision itself increase by applying a multiple perspectives approach rather than a single model. The multiple perspectives decision‐making allows collaboration and knowledge sharing among the participants which leads to a less‐biased decision. This study examines commonly applied supplier selection models, formulates general perspectives of these models, and pr...