Data Mining in Health-Care: Issues and a Research Agenda

While data mining has become a much-lauded tool in business and related fields, its role in the healthcare arena is still being explored. Currently, most applications of data mining in healthcare can be categorized into two areas: decision support for clinical practice, and policy planning/decision making. However, it is challenging to find empirical literature in this area since a substantial amount of existing work in data mining for health care is conceptual in nature. In this paper, we review the challenges that limit the progress made in this area and present considerations for the future of data mining in healthcare

Data Mining for Web-Enabled Electronic Business Applications

Web-enabled electronic business is generating massive amounts of data on customer purchases, browsing patterns, usage times, and preferences at an increasing rate. Data mining techniques can be applied to all the data being collected for obtaining useful information. This chapter attempts to present issues associated with data mining for Web-enabled electronicbusiness. Copyright Idea Group Inc

Social security data mining : an Australian case study

University of Technology, Sydney. Faculty of Engineering and Information Technology.Data mining in business applications has become an increasingly recognized and accepted area of enterprise data mining in recent years. In general, while the general principle and methodologies of data mining and machine learning are applicable for any business applications, it is often essential to develop specific theories, tools and systems for mining data in a particular domain such as social security and social welfare business. This necessity has led to the concept of social security and social welfare data mining, the focus of this thesis work. Social security and social welfare business involves almost every citizen’s life at different life periods. It provides fundamental and crucial government services and support to varied populations of specific need. A typical scenario in Australia is that it not only connects one third of our populations, but also associates with many relevant stakeholders, including banking business, taxation and Medicare. Such business engages complicated infrastructure, networks, mechanisms, policies, activities, and transactions. Data mining of such business is a brand new application area in the data mining community. Mining such social welfare business and data is challenging. The challenges come from the unavailable benchmark and experience in the data mining for this particular domain, the complexities of social welfare business and data, the exploration of possible doable tasks, and the implementation of data mining techniques in relation to the business objectives. In this thesis, which adopts a practice-based innovative attitude and focusses on the marriage of social welfare business with data mining, we believe we have realised our objective of providing a systematic and comprehensive overview of the social security and social welfare data mining. The main contributions consist of the following aspects: • As the first work of its kind, to the best of our knowledge, we present an overall picture of social security and social welfare data mining, as a new domain driven data mining application. • We explore the business nature of social security and social welfare, and the characteristics of social security data. • We propose a concept map of social security data mining, catering for main complexities of social welfare business and data, as well as providing opportunities for exploring new research issues in the community. • Several case studies are discussed, which demonstrate the technical development of social security data mining, and the innovative applications of existing data mining techniques. The nature of social welfare is spreading widely across the world in both developed and developing countries. This thesis work therefore is timely and could be of important business and government value for better understanding our people, our policies, our objectives, and for better services of those people of genuine needs

Supply chain intelligence: benefits, techniques and future trends

Supply Chain Management is a philosophy to manage logistical processes in complex systems, that are very difficult to integrate and analyze. Such systems can be effectively analysed by the use of Business Intelligence applications. The capability to make the right decision at the right time in collaboration with the right partners is the definition of the successful use of BI. This paper explains the need for Supply Chain Business Intelligence and introduces the driving forces for it’s implementation. New technologies such as data mining, and their role in BI systems are also discussed. Finally, key BI trends and technologies that will influence future systems are described.supply chain, business intelligence, data mining

A Critical Overview of Data Mining for Business Applications

Everybody looks to a world that does not remain the same. Furthermore no one can deny that the world is changing, and changing very fast. Technology, education, science, environment, health, communicating habits, entertainment, eating habits, dress - there is hardly anything in life that is not changing. Some changes we like, while others create fear and anxiety around us. Everywhere there is a feeling of insecurity. What will happen to us tomorrow, or what will happen to our children, are questions we keep frequently asking. One thing, however, is clear. It is no more possible to live in the way we have been living so far. It seems that now the entire fabric of life will have to be changed. Life will have to be redesigned. The life of the individual, the social structure, the working conditions and governance all will have to be re-planned. Furthermore over the past 2-3 decades there has been a huge increase in the amount of data being stored in databases as well as the number of database applications in business and the scientific domain. This explosion in the amount of electronically stored data was accelerated by the success of the relational model for storing data and the development and maturing of data retrieval and manipulation technologies. While technology for storing the data developed fast to keep up with the demand, little stress was paid to developing software for analysing the data until recently when companies realized that hidden within these masses of data was a resource that was being ignored. The huge amounts of stored data contains knowledge on a good number of aspects of their business waiting to be harnessed and used for more effective business decision support. Database Management Systems (DMS) used to manage these data sets at present only allow the user to access information explicitly present in the databases i.e. the data. The data stored in the database is only a small part of the \u27iceberg of information\u27 available from it. Contained implicitly within this data is knowledge about a number of aspects of their business waiting to be harnessed and used for more effective business decision support. This extraction of knowledge from large data sets is called Data Mining or Knowledge Discovery in Databases and is defined as the non-trivial extraction of implicit, previously unknown and potentially useful information from data. Almost in parallel with the developments in the database field, machine learning research was maturing with the development of a number of sophisticated techniques based on different models of human learning. Learning by example, cased-based reasoning, learning by observation and neural networks are some of the most popular learning techniques that were being used to create the ultimate thinking machine

Knowledge Discovery Database (KDD)-Data Mining Application in Transportation

In this paper, an understanding and a review of data mining (DM) development and its applications in logistics and specifically transportation are highlighted. Even though data mining has been successful in becoming a major component of various business processes and applications, the benefits and real-world expectations are very important to consider. It is also surprising to note that very little is known to date about the usefulness of applying data mining in transport related research. From the literature, the frameworks for carrying out knowledge discovery and data mining have been revised over the years to meet the business expectations. In this paper, we apply CRISP-DM for formulating effective tire maintenance strategy within the context of a Malaysian's logistics company. The results of applying CRISP-DM for tire maintenance decisions are presented and discussed

Evaluation of the Contemporary Issues in Data Mining and Data Warehousing

Over the past years data warehousing and data mining tools have evolved from research into a unique and popular business application class for decision support and business intelligence. This paper focuses on presenting the applications of data mining in the business environment. It contains a general overview of data mining, providing a definition of the concept, enumerating six primary data mining techniques and mentioning the main fields for which data mining can be applied. The paper also presents the main business areas which can benefit from the use of data mining tools, along with their use cases: retail, banking and insurance. Also the main commercially available data mining tools and their key features are presented within the paper. Theoretical and empirical literature was reviewed and various gaps in literature were identified. Besides the analysis of data mining and the business areas that can successfully apply it, the paper suggested and concluded that firms and scholars need to carry out more empirical research in the area of integrity of data mining and data warehousing since this will help eliminate marketing errors in operations and practice

Knowledge Discovery Database (KDD)-Data Mining Application in Transportation

In this paper, an understanding and a review of data mining (DM) development and its applications in logistics and specifically transportation are highlighted. Even though data mining has been successful in becoming a major component of various business processes and applications, the benefits and real-world expectations are very important to consider. It is also surprising to note that very little is known to date about the usefulness of applying data mining in transport related research. From the literature, the frameworks for carrying out knowledge discovery and data mining have been revised over the years to meet the business expectations. In this paper, we apply CRISP-DM for formulating effective tire maintenance strategy within the context of a Malaysian’s logistics company. The results of applying CRISP-DM for tire maintenance decisions are presented and discussed