بسمه تعالی داده کاوی و کشف دانش محمد تقی پور دارای مدارک تحصیلی: کاردانی،کارشناسی،کارشناسی ارشد آمار و دکترای مهندسی صنایع مدیرگروه مهندسی صنایع غیرانتفاعی آبا استاد نمونه دانشگاه های آزاد و پیام نور [email protected] www.drmohamadtaghipour.ir www.mtaghipour.tk 09123944126.
Evaluation of an integrated Knowledge Discovery and Data Mining process model
Limitations of previous KDDM models Mining process model
Checklist oriented description and lack of tool support
Absence of an integrated view
4. Conspicuous lack of support for tasks of the business understanding phase
A static analysis helps in evaluation of a design artifact on the basis of static or desired qualities.
The model incorporates the Mining process modelsame dimensions as Seddon’s model:
1. Identified and recruited 42 study participants and randomly divided them in two groups.
2. Presented one group of users with a test questionnaire, which includes Data Mining tasks posed as multiple choice questions.
3. Mining process model After the completion of the test questionnaire, recorded the perception of the static qualities of the artifact (i.e. the CRISP-DM or the IKDDM process model) used by each participant through a set of survey questions (Refer Table 3).
4. Recorded each participant’s gender, role/designation, number of years of experience in Data Mining, and time taken to complete the test. A numeric id was used to link the responder’s test to the survey. No identifying detail, such as name of the participant, or name of the organization that the individual is affiliated were recorded.
5. Mining process model Tested for statistical differences in the quality of the two models, as perceived by the users. The independent meanst-test as well as the Mann Whitney procedure was used to test the differences between the two groups (IKDDM versus CRISP-DM).
The following Mininginformation was recorded for each participant:
Results of independent means t-test – analysis of performance of CRISP-DMeval versus IKDDMeval on test questionnaire: using independent mean t-test
(p < 0.001) for the perceived ease of use scores of the two groups(refer table13).
(p < 0.001) for the user satisfacation scores of the two groups(refer table13).
(p < 0.001) for the perceived usefulness scores of the two groups(refer table13).
(p < 0.001) for the perceived semantic quality scores of the two groups(refer table13).