Data Mining and Rasch Measurement: "Data Mining and Rasch Measurement CRISP-DM, Linacre J.M. Rasch Measurement Transactions, Fall 2001, 15:2 p. 826-7
Data mining is finding useful relationships in large datasets. 'When you mine data (by 'drilling down'), you use data to improve your business by predicting and understanding behavior.' (Peter Frometa, SPSS Inc., 2001)
According to a press release, 'in May 1998, more than 20 key players in the data mining market met to discuss the first draft of a new process model, CRISP-DM ('CRoss-Industry Standard Process for Data Mining'). This is designed to help businesses plan and work through the complete data mining process - from problem specification to deployment of results. The core consortium consists of NCR, ISL, Daimler-Benz and OHRA. At the centre of the CRISP-DM project is a Special Interest Group (SIG) of data mining service suppliers and large-scale commercial users.'
Data mining employs a 6-stage approach to extracting meaning from business data. This parallels Rasch-based approaches to measurement construction in the social sciences. The Table below focusses on the Data Cleaning component of data mining. It is in marked contrast to the conventional 'data is inviolable' approach of social science research."
Data mining is finding useful relationships in large datasets. 'When you mine data (by 'drilling down'), you use data to improve your business by predicting and understanding behavior.' (Peter Frometa, SPSS Inc., 2001)
According to a press release, 'in May 1998, more than 20 key players in the data mining market met to discuss the first draft of a new process model, CRISP-DM ('CRoss-Industry Standard Process for Data Mining'). This is designed to help businesses plan and work through the complete data mining process - from problem specification to deployment of results. The core consortium consists of NCR, ISL, Daimler-Benz and OHRA. At the centre of the CRISP-DM project is a Special Interest Group (SIG) of data mining service suppliers and large-scale commercial users.'
Data mining employs a 6-stage approach to extracting meaning from business data. This parallels Rasch-based approaches to measurement construction in the social sciences. The Table below focusses on the Data Cleaning component of data mining. It is in marked contrast to the conventional 'data is inviolable' approach of social science research."
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