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client/server(C/S) systems have revolutionized the systems development approach. Among the drivers of the C/S systems is the lower price/performance ratio compared to the mainframe‐based transaction processing systems. Data mining is a process of identifying patterns in corporate transactional and operational databases (also called data warehouses). As most Fortune 500 companies are moving quickly towards the client server systems, it is increasingly becoming important that a data mining approaches should be adapted for C/S systems. In the current paper, we describe different data mining approaches that are used in the C/S systems.

Numerous research questions in e-HRM research are directly related to the usage of diverse information systems by HR professionals, line managers, employees, and/or applicants. Since they are regularly based on Internet technologies, information systems in e-HRM automatically store detailed usage data in log files of web servers. Subsumed as “web mining,” such data are frequently used as inputs for innovative data analysis in e-commerce practice. Though also promising in empirical e-HRM research, web mining is neither discussed nor applied in this area at present. Our chapter therefore aims at a methodological evaluation of web mining as an e-HRM research approach. After introducing web mining as a possible approach in e-HRM research, we examine its applicability by discussing available data, feasible methods, coverable topics, and confirmable privacy. Subsequently, we classify the approach methodologically by examining major issues. Our evaluation reveals that “web mining” constitutes a promising additional research approach that enables research to answer numerous relevant questions related to the actual usage of information systems in e-HRM.

Learning management systems (LMS) provide detailed information about the processes through event-logs. Process and related data-mining approaches can reveal valuable information from these files to help teachers and executives to monitor and manage their online learning processes. In this regard, the purpose of this paper is to present an overview of the current direction of the literature on educational data mining, and an application framework to analyze the educational data provided by the Moodle LMS.

The paper presents a framework to provide a decision support through the approaches existing in process and data-mining fields for analyzing the event-log data gathered from LMS platforms. In this framework, latent class analysis (LCA) and sequential pattern mining approaches were used to understand the general patterns; heuristic and fuzzy approaches were performed for process mining to obtain the workflows and statistics; finally, social-network analysis was conducted to discover the collaborations.

The analyses conducted in the study give clues for the process performance of the course during a semester by indicating exceptional situations, clarifying the activity flows, understanding the main process flow and revealing the students’ interactions. Findings also show that using the preliminary data analyses before process mining steps is also beneficial to understand the general pattern and expose the irregular ones.

The study highlights the benefits of analyzing event-log files of LMSs to improve the quality of online educational processes through a case study based on Moodle event-logs. The application framework covers preliminary analyses such as LCA before the use of process mining algorithms to reveal the exceptional situations.

Data mining has been a popular research area in the past decades. Many researchers study data-mining theories, methods, applications and trends; however, there are very few studies on data-mining-related topics in social media. This paper aims to explore the topics related to data mining based on the data collected from Wikipedia.

In total, 402 data-mining-related articles were obtained from Wikipedia. These articles were manually classified into several categories by the coding method. Each category formed an article-term matrix. These matrices were analysed and visualized by the self-organizing map approach. Several clusters were observed in each category. Finally, the topics of these clusters were extracted by content analysis.

The articles obtained were classified into six categories: applications, foundation and concepts, methodologies, organizations, related fields and topics and technology support. Business, biology and security were the three prominent topics of the applications category. The technologies supporting data mining were software, systems, databases, programming languages and so forth. The general public was more interested in data-mining organizations than the researchers. They also focused on the applications of data mining in business more than in other fields.

This study will help researchers gain insight into the general public’s perceptions of data mining and discover the gap between the general public and themselves. It will assist researchers in finding new techniques and methods which will potentially provide them with new data-mining methods and research topics.

A major challenge facing management in developed countries is improving the performance of knowledge and service workers, i.e. the decision and policy makers. In a developing country such as South Africa, with a well‐developed business sector, this need, especially in government, is even more crucial. South Africa has to face many new challenges in the 21st century ‐ growing environmental concerns, massive social and economic inequalities, high occurrences of HIV, low productivity, massive unemployment and the nation’s evolving role in Africa, amongst others. The importance of a sound science and technology policy framework to address these pressing issues cannot be overemphasised This paper discusses the construction of a knowledge‐base from a data repository concerning a South African National Research and Technology (NRT) Audit. This knowledge‐base is to be used as an aid when developing a science and technology policy framework for South Africa. The knowledge‐base is constructed using the cooperative inductive learning team (CILT) approach, which combines diverse data mining tools and human expertise into a cooperative learning system. In this approach, each data mining tool constructs a model of the knowledge as contained in the data repository, thus providing an automated tool to make sense of the knowledge embedded therein. That is, the data mining tools learn from the data in order to obtain new insights. The system also incorporates human domain expertise through the computational modelling of the human subject knowledge. The knowledge, as obtained during team learning, is stored in a team knowledge‐base. Results indicate that the CILT learning team approach can be successfully used to make sense of the vast amounts of data collected and provide a knowledge repository for further decision making and policy formulation.

The data created from regular maintenance activities of equipment are stored as text in industrial plants. The size of these data is increasing rapidly nowadays. Text mining provides a chance to handle this huge amount of text data and extract meaningful information to improve various processes of an industrial environment. This paper represents the application of classification models on maintenance text records to classify failure for improving maintenance programs in the industry.

This paper is presented as an implementation study, where text mining approaches are used for binary classification of text data. Naive Bayes and Support Vector Machine (SVM), two classification algorithms are applied for training and testing of the models as per the labeled data. The reason behind this is, these algorithms perform better on text data for classifying failure and they are easy to handle. A methodology is proposed for the development of maintenance programs, including classification of potential failure in advance by analyzing the regular maintenance data as well as comparing the performance of both models on the data.

The accuracy of both models falls within the acceptable limit, and performance evaluation of the models concludes the validation of the results. Other performance measures exhibit excellent values for both of the models.

The proposed approach provides the maintenance team an opportunity to know about the upcoming breakdown in advance so that necessary measures can be taken to prevent failure in an industrial environment. As predictive maintenance incurs a high expense, it could be a better replacement for small and medium industrial plants.

Nowadays, maintenance is preventive-based rather than a corrective approach. The proposed technique is facilitating the concept of a proactive approach by minimizing the cost of additional maintenance steps. As predictive maintenance is efficient but incurs high expenses, this proposed method can minimize unnecessary maintenance operations and keep control over the budget. This is a significant way of developing maintenance programs and will make maintenance personnel ready for the machine breakdown.

The purpose of this paper is to use statistical techniques to mine and analyze suspicious transactions. With the increase in money laundering activities across various sectors in some of the world’s leading democracies, the ability to detect such transactions is gaining grounds with more urgency. Regulators and practitioners have been calling for an approach that can mine the large volume of unstructured data form suspicious money laundering transactions to inform public policies.

By deducing from the results of empirical studies in the field of money laundering detection, this paper presented an overview of data mining technology for detecting suspicious transactions.

After chronicling the data mining process, the paper delves into an analysis of the statistical approaches that can be used to differentiate between legitimate and suspicious money laundering transactions. The different stages of the data mining process are carefully explained in relation to their application to anti-money laundering compliance. The results indicate that statistical data mining methodology is a very efficient and useful technique to detect suspicious transactions.

The paper is of relevance to regulators and the financial service sector. A discussion of how data can be mined to facilitate statistical analysis can be used to inform regulatory policies on the detection and prevention of money laundering activities in the financial service sector.

The paper discuss approaches that illustrate how analysts can use statistical techniques to analyze data for suspicious money laundering transactions

In terms of service hospitality, recent discussions of value-in-use from the perspective of service-dominant logic have focused on the customer’s determination of value and control of the value creation process. The purpose of this paper is to extend these discussions by exploring the value creation process in the Western-style restaurant in Taiwan, which is developed value-in-eat creation for restaurants. In Taiwan, Western-style restaurants are as popular as Chinese restaurants because of globalization and cultural integration. However, to local restaurateurs and managers, managing a Western-style restaurant in terms of localization and hospitality on value-in-eat creation presents both academic and practical issues. Thus, this paper aims to investigate Western-style restaurant hospitality management alternatives on the value-in-eat creation process in Taiwan using a data mining approach.

Based on a market survey, a total of 1,187 questionnaires was incorporated into a database. The questionnaire design is divided into 7 parts with 35 items. All questions are designed as nominal and ordinal (not the Likert scale) scales. Data mining approach, including cluster analysis and association rules, cluster analysis is investigated possible customer profiles and association rules is implemented to explore customer preference patterns and rules on the value-in-eat creation process.

Data mining results show two patterns including Pattern 1: meal patterns and customer preferences for restaurant hospitality management and Pattern 2: customer relationship management (CRM) for restaurant hospitality management that customer profiles and preferences on meal patterns, service patterns and CRM are engaged to suggest effective Western restaurant hospitality management alternatives, such as proper bundles for restaurant types, meals, exotic atmosphere and services of hospitalities in terms of a value-in-eat creation process.

To the best of the authors’ knowledge, this study is the first study to investigate consumers’ behaviors in Western-style restaurants using the measurement of nominal and ordinal scale for questionnaire development and further to implement a data mining approach on selected data samples. In addition, this study illustrates the patterns/rules of Taiwan customer preferences that best explain the knowledge of how to manage Western-style restaurants from the perspective of customer hospitality using data mining.

在酒店服务领域, 近期的从服务主流逻辑为视角关于使用价值的讨论主要集中在消费者对价值的定义以及掌控价值创造的过程。本研究的主要目的是拓展这些相关的讨论从而发掘关于在台湾经营的西餐厅的顾客价值创造过程, 进而开发餐厅的饮食价值创造。由于全球化进程, 台湾的西餐厅和中餐厅同样受欢迎。然而, 对于本地的餐厅所有人和经营管理者来讲, 管理西餐厅关于价值创造过程中的地方化和服务管理还存在学术和实践问题。因此, 本文运用了数据挖掘的方法对西餐关于价值创造的另类途径进行了探索。

基于市场调研, 本研究导入了1187份问卷作为数据库。问卷由7部分35项条目组成。所有问题以称名量表和顺序量表（非李克特量表）测量。数据挖掘包括了聚类分析和关联分析。聚类分析用来分析消费者概况, 关联分析来探究顾客倾向以及饮食价值创造过程。

数据挖掘结果显示了两种模式, 1：食物以及顾客对餐厅的接待管理的偏爱以及模式, 2：客户关系管理包括顾客概况和对饮食模式的偏爱, 服务模式以及顾客关系维护的另类建议, 诸如适度的捆绑销售包括餐厅种类, 菜系, 异域风情的就餐环境以及服务来体现饮食价值创造的过程。

本研究是首次探索了用称名量表和顺序量来研发的消费者问卷并且运用了数据挖掘的方法研究了西餐厅的消费者行为。 此外, 本研究阐明了台湾消费者的偏爱模式从而更好的解释了如何用数据挖掘的方法来研究西餐厅的服务管理。

The purpose of this paper is to propose a novel approach based on utility mining for store layout.

A utility mining-based data mining algorithm is utilized in this paper.

A real-life case study in a supermarket is conducted to illustrate the proposed approach. The findings show that the proposed approach can be used easily and efficiently to arrange store layout.

There are two limitations to this study. First, space allocation to each product family is not considered. Second, product placement in each product family is not taken into account in the proposed approach.

In this paper, a novel approach is proposed for business intelligence in retail business. The proposed approach uses a utility-based data mining approach, namely, the high-utility itemset mining (HUIM) algorithm, to rearrange store layout and to determine the relations among product families. The quantities and prices of items purchased corresponding to product families are taken into account in the proposed approach to address the needs in practice. Business intelligence software is also developed as an integral part of the proposed approach to utilize the HUIM algorithm.

The General Society Survey(GSS) is a kind of government-funded survey which aims at examining the Socio-economic status, quality of life, and structure of contemporary society. GSS data set is regarded as one of the authoritative source for the government and organization practitioners to make data-driven policies. The previous analytic approaches for GSS data set are designed by combining expert knowledges and simple statistics. By utilizing the emerging data mining algorithms, we proposed a comprehensive data management and data mining approach for GSS data sets.

The approach are designed to be operated in a two-phase manner: a data management phase which can improve the quality of GSS data by performing attribute pre-processing and filter-based attribute selection; a data mining phase which can extract hidden knowledge from the data set by performing data mining analysis including prediction analysis, classification analysis, association analysis and clustering analysis.

According to experimental evaluation results, the paper have the following findings: Performing attribute selection on GSS data set can increase the performance of both classification analysis and clustering analysis; all the data mining analysis can effectively extract hidden knowledge from the GSS data set; the knowledge generated by different data mining analysis can somehow cross-validate each other.

By leveraging the power of data mining techniques, the proposed approach can explore knowledge in a fine-grained manner with minimum human interference. Experiments on Chinese General Social Survey data set are conducted at the end to evaluate the performance of our approach.