论文信息 - Data Mining Processes and Collaboration Principles

Data Mining Processes and Collaboration Principles

Data mining is a process involving the application of human skill as well as technology, and as such it can be supported by clearly defined processes and procedures. This chapter presents the CRISP-DM process, one well developed standard data mining process, which contains clearly defined phases with clearly defined steps and deliverables. The nature of some of the CRISP-DM phases is such that it is possible to perform them in an e-collaboration setting. The principles for extending the CRISP-DM process to support collaborative data mining are described in the RAMSYS approach to data mining. The tools, systems, and evaluation procedures that are required for the RAMSYS approach to reach its potential are described.

[1] A. K. Pujari,et al. Data Mining Techniques , 2006 .

[2] Angi Voß. E-discourses with Zeno , 2002, Proceedings. 13th International Workshop on Database and Expert Systems Applications.

[3] Angi Voß,et al. Collaboration Support for Virtual Data Mining Enterprises , 2001, LSO.

[4] Dorian Pyle,et al. Data Preparation for Data Mining , 1999 .

[5] Ersin Cem Kaletas. Virtual Laboratories and Virtual Organizations Supporting Biosciences , 2002, PRO-VE.

[6] David J. Hand,et al. Intelligent Data Analysis: An Introduction , 2005 .

[7] Michael J. A. Berry,et al. Data mining techniques - for marketing, sales, and customer support , 1997, Wiley computer publishing.

[8] Hendrik Blockeel,et al. Collaborative data mining needs centralised model evaluation , 2002, ICML 2002.

[9] Alípio Mário Jorge. Remote Collaborative Data Mining Through Online Knowledge Sharing , 2002, PRO-VE.

[10] Padhraic Smyth,et al. From Data Mining to Knowledge Discovery: An Overview , 1996, Advances in Knowledge Discovery and Data Mining.

[11] Pieter Adriaans,et al. Data mining , 1996 .