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.
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Data Mining projects are implemented by following the knowledge discovery process.
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 Information Quality of a conceptual model users is the perceived semantic quality of the model such as how valid and complete it is with respect to (their perception of) the problem domain.
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. 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. 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).
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).
The results of Mann–Whitney test on the overall survey scores representing the quality of the process models indicate that a significant difference existed between the CRISP and IKDDM models.
The results of Mann–Whitney test across the four constructs also indicated that the IKDDM group and CRISP group significantly differed in their perceptions of ease of use, usefulness, semantic quality and levels of user satisfaction of the model employed by them to execute tasks in Data Mining.
The IKDDM group reported significantly higher levels of perceived ease of use,perceived usefulness, semantic quality and user satisfaction as compared to the CRISP group.