Java Data Mining: Strategy, Standard, and Practice: A Practical Guide for Architecture, Design, and ImplementationElsevier, 2010 M07 26 - 544 pages Whether you are a software developer, systems architect, data analyst, or business analyst, if you want to take advantage of data mining in the development of advanced analytic applications, Java Data Mining, JDM, the new standard now implemented in core DBMS and data mining/analysis software, is a key solution component. This book is the essential guide to the usage of the JDM standard interface, written by contributors to the JDM standard.
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algorithm analysis apply task associated attribute importance attribute name Attriter BPEL build data build settings BuildTask campaign Chapter classification model clustering model compute confusion matrix connection ConnectionFactory cost matrix create CRISP-DM data mining engine data mining model data mining process data mining standards data mining tools data preparation data warehouse database decision tree defined example execution Figure identify input objects Java Data Mining JAX-RPC JDBC JDM API JDM implementation JDM Schema JDM standard JDM Web services JDMWS KXEN Listing logical data Methods Description mining function mining objects model building node Non-attriter null OJDM operation Oracle Oracle Data Mining package percent PhysicalDataSet PMML prediction problem provides regression response retrieve rules scoring specified statistics String support vector machine Table target attribute target value test metrics throws JDMException tion transformations vendors Web services WSDL XML Schema
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