Java Data Mining: Strategy, Standard, and Practice

Data mining is becoming a mainstream technology used in business intelligence applications supporting industries such as financial services, retail, healthcare, telecommunications, and higher education, and lines of business such as marketing, manufacturing, customer experiences, customer service, and sales. Many of the business problems that data mining can solve cut across industries such as customer retention and acquisition, cross-sell, and response modeling. Due to the cost, skillsets, and complexity required to bring data mining results into an established business process, early adopters were typically big companies and research labs with correspondingly large budgets and access to statisticians and machine learning experts. In recent years, however, data mining products have simplified data mining considerably by automating the process—making the fruits of the technology more widely accessible. New algorithms and heuristics have evolved to provide good results with little or no experimentation or data preparation. In addition, the availability of data mining has increased with in-database data mining capabilities.

Java Data Mining (JDM) furthers the adoption of data mining by providing a standard Java and web services Application Programming Interface (API) for data mining. This book introduces data mining to software developers and application architects who may have heard of the benefits of data mining but are unsure how to realize these benefits. This book is also targeted at business and data analysts who want to learn how JDM helps in developing vendor-neutral data mining solutions. It does not require a reader to be familiar with data mining, statistics, or machine learning technologies.

We have organized this book into three main parts: strategy, standard, and practice. In Part I, JDM Strategy, we introduce data mining in general, uses of data mining in solving industry problems, data mining processes and techniques, the role of data mining standards, and a high-level introduction to the JDM Application Programming Interface (API). Most of this part doesn’t require the reader to know the Java language.

In Part II, JDM Standard, we explain the concepts used in JDM by example, explore the JDM API design and its usage, and introduce the Java Data Mining XML schema and web services. This part requires readers to know the Java language, XML, and XML schema. It gives a brief introduction to web services in Chapter 11 before discussing the JDM web services.

In Part III, JDM Practice, we illustrate practical problem solving using the JDM API. We begin by developing a sample data mining tool using JDM and a sample data mining web service using JDM. We then introduce two JDM vendor implementations, exploring their functionality, architecture, and design tradeoffs before giving some guidance to others interested in implementing a JDM-compliant
system.

In Part IV, Wrapping Up, we discuss the evolution of data mining standards, where they have been and where they might go. We give a preview of some of the features proposed for JDM 2.0.

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