The transformation of data into information is a major challenge for the decision maker. With the advancements in the use of information and communication technologies (ICTs), various methods have evolved to manage information. These methods are primarily aimed at the automation of data collection, retrieval, measurement, and analysis. Evolutionary relational and object-oriented features in database management systems and open database connectivity protocols have enhanced the feasibility of managing temporal data. Databases also provide the flexibility to capture and arrange data through various object-based and object-relational tools. These features have challenged data modelers to incorporate appropriate analytical tools and measurement approaches to enable common users to garner information on time with the help of relevant optimization techniques.

Data mining concepts provide the impetus for optimization techniques. Contemporary data mining challenges include identifying proper optimization methods for temporal data and linking the data to the decision-making process. Trends in data mining methods and techniques are in the areas of distributed data mining, hypertext and hypermedia data mining, Web-based data mining, spatial and geographical data mining, and constraint-based data mining. This trend is toward enhancing end-user capabilities in using data mining techniques seamlessly for better decision making. In contemporary business scenarios, it is now quite common to integrate data mining tools with ICT-enabled business processes that are mostly interdisciplinary and dynamic. Thus, there is a need for convergence in creating user-centered data mining applications and techniques to facilitate interdisciplinary business decisions. Algorithmic presentations of such techniques are quite beneficial for data mining because data modeling for decision making is always dynamic and because efficient data mining techniques look for good data models.

This book on data mining discusses various contemporary approaches through the presentation of algorithms and their applications, especially using multiple criteria programming (MCP) and support vector machines (SVMs). The strength of the book lies in providing a repository of data mining algorithms followed by their applications in various fields, including financial analyses, personal credit management, health insurance fraud detection, HIV-1 informatics, and geochemical analyses. A comparative analysis of both MCP and SVM approaches provides an interesting dimension to the whole process of data mining applications. The book thus meets the expectations of business analysts and researchers interested in adopting best practices for data mining. This book also provides some scope for understanding the convergence features that could bring business analysis imperatives.

However, the book lacks any perspective for the common reader who wants be introduced to data mining applications. It caters to data miners who are knowledgeable about the related fundamentals. A section explaining the rationale for choosing MCP- and SVM-based optimization techniques, presenting building blocks of data mining processes, and establishing relationships among various techniques for intelligent knowledge management would have enhanced the readability and completeness of the book.

One of the major contributions is the presentation of algorithms; however, explanations of the algorithms through the benchmarking approach would have been quite beneficial. Algorithms are subjected to debates on their reliability, efficiency, and effectiveness. Reliable, efficient, and effective algorithms contribute to data mining principles because of the inherent demand for optimization of multidimensional search, retrieval, and analytical techniques. A section on comparative analyses of the algorithms for their appropriateness and their applications in real-life situations would have also enhanced the book.