Journal of Taiwan Normal University Humanities & Social Sciences 2005,50( 1 ),89-1 06
A Study of Swimming Member Chum Model
U sing Data Mining Classification Techniques
Chan-ping Lin
Chih-pin Shih
Hsin-pu Junior High School Department of Physical Education, NTNU
Abstract
The purpose of this study was to construct a model for analyzing the tumover rate among those who become members of the NTNU main campus swimming pool, in order to facilitate the dirninishing, on the part of the swimrning pool adrninistrative staff, of member dropout and tumover. This model was constructed by using data rnining c1assification technology inc1uding discrirnination analysis, artificial neural networks, multivariate adaptive regression splines, and multivariate adaptive regression splines. The first step was of course to establish the salient characteristics of swimming pool member tumover.
After reorganizing the data for all swimmers in the NTNU main campus swimrning pool, 2,707 records were chosen as our initial data. Aftβr deleting the unreasonable data, a total of 2,380 records were discussed in this study. The research results were as follows:
1. As for the over-all constitutive structure of this group: (1) the number of male members (49.879毛) and female members (50.13%) was almost equa1; (2) 55.63% were
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free" members directly connected to NTNU; (3) most members were residents of Da-an and Zhongzheng districts (79.12%); (4) the average age of members was 32.15 years, and there was a roughly even age distribution; (5) 出eaverage participation period of members was 0.69 years and most joined for less than 2 years (93.66%); (6) the vast majority were non-discount members (95.80%); (7) the most frequent1y levied membership fee was 4,500 NT dollars (42.86%); (8) the summer season was easily the most popular for enrollment (49.669品); (9) no lirnitation on times when the pool could be used was the most popular choice (56.64%).
2. Combining artificial neura1 networks and multivariate adaptive regression splines generated a c1assification rate of 84.03%. The integrated approach successfully constructed a member chum model for the NTNU main campus swimming pool.
3. The optimal characteristics of an NTNU main campus swimrning pool member were: participation period below 1 year, member fee of 2,500 NT dollars, membership enrollment in summer, and gen巳ralmembers.
It is hoped that the NTNU Swimming Pool Adrninistration Dep征tment can use these results in order to decrease member dropout and tumover
Key Words: Data Mining, Member tumover, Discrirnination Analysis, Artificial Neural Networks, Multivariate Adaptive Regression Splines
