Early Warning System for SMEs as a Financial Risk Detector

This chapter introduces an early warning system for SMEs (SEWS) as a financial risk detector which is based on data mining. In this study, the objective is to compose a system in which qualitative and quantitative data about the requirements of enterprises are taken into consideration, during the development of an early warning system. Furthermore, during the formation of system; an easy to understand, easy to interpret and easy to apply utilitarian model that is far from the requirement of theoretical background is targeted by the discovery of the implicit relationships between the data and the identification of effect level of every factor. Using the system, SME managers could easily reach financial management, risk management knowledge without any prior knowledge and expertise. In other words, experts share their knowledge with the help of data mining based and automated EWS.

Mining Allocating Patterns in Investment Portfolios

An association rule (AR) is a common type of mined knowledge in data mining that describes an implicative co-occurring relationship between two sets of binary-valued transaction-database attributes, expressed in the form of an ? rule. A variation of ARs is the (WARs), which addresses the weighting issue in ARs. In this chapter, the authors introduce the concept of “one-sum” WAR and name such WARs as allocating patterns (ALPs). An algorithm is proposed to extract hidden and interesting ALPs from data. The authors further indicate that ALPs can be applied in portfolio management. Firstly by modelling a collection of investment portfolios as a one-sum weighted transaction- database that contains hidden ALPs. Secondly the authors show that ALPs, mined from the given portfolio-data, can be applied to guide future investment activities. The experimental results show good performance that demonstrates the effectiveness of using ALPs in the proposed application.

Introduction to Data Mining Techniques via Multiple Criteria Optimization Approaches and Applications

This chapter provides an overview of a series of multiple criteria optimization-based data mining methods, which utilize multiple criteria programming (MCP) to solve data mining problems, and outlines some research challenges and opportunities for the data mining community. To achieve these goals, this chapter first introduces the basic notions and mathematical formulations for multiple criteria optimization- based classification models, including the multiple criteria linear programming model, multiple criteria quadratic programming model, and multiple criteria fuzzy linear programming model. Then it presents the real-life applications of these models in credit card scoring management, HIV-1 associated dementia (HAD) neuronal damage and dropout, and network intrusion detection. Finally, the chapter discusses research challenges and opportunities.

The Importance of Data Within Contemporary CRM

In recent times, customer relationship management (CRM) has been defined as relating to sales, marketing, and even services automation. Additionally, the concept is increasingly associated with cost savings and streamline processes as well as with the engendering, nurturing and tracking of relationships with customers. Much less associations appear to be attributed to the creation, storage and mining of data. Although successful CRM is in evidence based on a triad combination of technology, people and processes, the importance of data is unquestionable. Accordingly, this chapter seeks to illustrate how, although the product and service elements as well as organizational structure and strategies are central to CRM, data is the pivotal dimension around which the concept revolves in contemporary terms. Consequently, this chapter seeks to illustrate how the processes associated with data management, namely: data collection, data collation, data storage and data mining, are essential components of CRM in both theoretical and practical terms.

Medical Applications of Nanotechnology in the Research Literature

The medical applications literature associated with nanoscience and nanotechnology research was examined. About 65,000 nanotechnology records for 2005 were retrieved from the Science Citation Index/ Social Science Citation Index (SCI/SSCI) using a comprehensive 300+ term query. The medical applications were identified through a fuzzy clustering process. Metrics associated with research literatures for specific medical applications/ applications groups were generated.

Making Decisions with Data

This chapter identifies important barriers to the successful application of computational intelligence (CI) techniques in a commercial environment and suggests a number of ways in which they may be overcome. It identifies key conceptual, cultural and technical barriers and describes the different ways in which they affect both the business user and the CI practitioner. The chapter does not provide technical detail on how to implement any given technique, rather it discusses the practical consequences for the business user of issues such as non-linearity and extrapolation. For the CI practitioner, we discuss several cultural issues that need to be addressed when seeking to find a commercial application for CI techniques. The authors aim to highlight to technical and business readers how their different expectations can affect the successful outcome of a CI project. The authors hope that by enabling both parties to understand each other’s perspective, the true potential of CI can be realized.

What Role is "Business Intelligence" Playing in Developing Countries? A Picture of Brazilian Companies

Constant technological innovation and increasing competitiveness make the management of information a considerable challenge, requiring decision-making processes built on reliable and timely information from internal and external sources. Although available information increases, this does not mean that people automatically derive value from it. After years of significant investment to establish a technological platform that supports all business processes and strengthens the operational structure’s efficiency, most organizations are supposed to have reached a point where the implementation of information technology (IT) solutions for strategic purposes becomes possible and necessary. This explains the emergence of “business intelligence” (BI); a response to information needs for decision-making through intensive IT use. This chapter looks at BI projects in developing countries – specifically, in Brazil. If the management of IT is a challenge for companies in developed countries, what can be said about organizations struggling in unstable contexts such as those often prevailing in developing countries?

Business Data Warehouse

The retailing giant Wal-Mart owes its success to the efficient use of information technology in its operations. One of the noteworthy advances made by Wal-Mart is the development of the data warehouse which gives the company a strategic advantage over its competitors. In this chapter, the planning and implementation of the Wal-Mart data warehouse is described and its integration with the operational systems is discussed. The chapter also highlights some of the problems encountered in the developmental process of the data warehouse. The implications of the recent advances in technologies such as RFID, which is likely to play an important role in the Wal-Mart data warehouse in future, is also detailed in this chapter.

Image Mining

Daily, different satellites capture data of distinct contexts, which images are processed and stored in many institutions. This chapter presents relevant definitions on remote sensing and image mining domain, beyond referring to related work on this field and to the importance of appropriate tools and techniques to analyze satellite images and extract knowledge from this kind of data. The Amazonia deforestation problem is discussed, as well INPE’s effort to develop and spread technology to deal with challenges involving Earth observation resources. An image mining approach is presented and applied on a case study, detecting patterns of change on deforested areas of Amazonia. The purpose of the authors is to present relevant technologies, new approaches and research directions on remote sensing image mining, demonstrating how to increase the analysis potential of such huge strategic data.

Data Mining Association Rules for Making Knowledgeable Decisions

This chapter describes two techniques used to explore frequent large itemsets in the database. In the first technique called “closed directed graph approach,” the algorithm scans the database once making a count on possible 2-itemsets from which only the 2-itemsets with a minimum support are used to form the closed directed graph which explores possible frequent large itemsets in the database. In the second technique, dynamic hashing algorithm, large 3-itemsets are generated at an earlier stage which reduces the size of the transaction database after trimming and the cost of later iterations will be less. Furthermore the authors hope that these techniques help researchers not only to understand about generating frequent large itemsets, but also assist with the understanding of finding association rules among transactions within relational databases.