Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
C4.5: programs for machine learning
C4.5: programs for machine learning
Fast discovery of association rules
Advances in knowledge discovery and data mining
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Launched in 1994, SPSS' Clementine was one of the first commercially available tools for data mining. Clementine uses an interface style known as visual programming in which numerous tools used in different stages of the data mining process are represented by icons that are strung together to form visual streams of data mining solutions. Streams can include machine learning and other modeling technologies, data access facilities, record and field manipulation, interactive visualization, conventional analyses, and reporting. The main aim of Clementine was to put data mining in the hands of business users, rather than just technology specialists. This is achieved partly by the easy-to-learn visual programming interface, and also by insulating users from technology details; by default, the modeling algorithms are automatically configured to suit a given data set. Clementine has also proved popular with more sophisticated power analyst users. For them, the visual programming environment provides a highly productive, fluent environment. Compared to a component-based approach, the proportion of the analysts' time needed for noncore tasks such as data manipulation and transformation is reduced, freeing them to concentrate on building models to solve problems. Both types of users value Clementine's openness, which enables integration with existing IT infrastructure, and comprehensive approaches to scalability and deployment of solutions. These approaches take into account all the stages in the data mining process so that, for example, data processing steps are scaled and all steps are exported for deploying solutions to decision makers. Clementine is used by hundreds of organizations worldwide, in sectors including finance, retail, telecommunications, government, and manufacturing. Many applications involve marketing and other aspects of customer relationship management (CRM), but the product has also been used to tackle data mining problems such as product quality analyses, toxicity prediction, and fraud detection.