输血服务中心数据集

Original Owner and Donor Prof. I-Cheng Yeh Department of Information Management Chung-Hua University, Hsin Chu, Taiwan 30067, R.O.C. e-mail:icyeh '@' chu.edu.tw TEL:[phone] Date Donated: October 3, 2008 Data Set Information: To demonstrate the RFMTC marketing model (a modified version of RFM), this study adopted the donor database of Blood Transfusion Service Center in Hsin-Chu City in Taiwan. The center passes their blood transfusion service bus to one university in Hsin-Chu City to gather blood donated about every three months. To build a FRMTC model, we selected 748 donors at random from the donor database. These 748 donor data, each one included R (Recency - months since last donation), F (Frequency - total number of donation), M (Monetary - total blood donated in c.c.), T (Time - months since first donation), and a binary variable representing whether he/she donated blood in March 2007 (1 stand for donating blood; 0 stands for not donating blood). Attribute Information: Given is the variable name, variable type, the measurement unit and a brief description. The "Blood Transfusion Service Center" is a classification problem. The order of this listing corresponds to the order of numerals along the rows of the database. R (Recency - months since last donation), F (Frequency - total number of donation), M (Monetary - total blood donated in c.c.), T (Time - months since first donation), and a binary variable representing whether he/she donated blood in March 2007 (1 stand for donating blood; 0 stands for not donating blood). Table 1 shows the descriptive statistics of the data. We selected 500 data at random as the training set, and the rest 248 as the testing set. Table 1. Descriptive statistics of the data Variable Data Type Measurement Description min max mean std Recency quantitative Months Input 0.03 74.4 9.74 8.07 Frequency quantitative Times Input 1 50 5.51 5.84 Monetary quantitative c.c. blood Input 250 12500 1378.68 1459.83 Time quantitative Months Input 2.27 98.3 34.42 24.32 Whether he/she donated blood in March 2007 binary 1=yes 0=no Output 0 1 1 (24%) 0 (76%) Relevant Papers: Yeh, I-Cheng, Yang, King-Jang, and Ting, Tao-Ming, "Knowledge discovery on RFM model using Bernoulli sequence," Expert Systems with Applications, 2008, [Web link] Citation Request: NOTE: Reuse of this database is unlimited with retention of copyright notice for Prof. I-Cheng Yeh and the following published paper: Yeh, I-Cheng, Yang, King-Jang, and Ting, Tao-Ming, "Knowledge discovery on RFM model using Bernoulli sequence, "Expert Systems with Applications, 2008, [Web link]