%% Title: Blood Transfusion Service Center Data Set %% %% Abstract: Data taken from the Blood Transfusion Service Center in Hsin-Chu City %% in Taiwan -- this is a classification problem. %% %% %% ----------------------------------------------------- %% %% Data Set Characteristics: Multivariate %% Number of Instances: 748 %% Area: Business %% Attribute Characteristics: Real %% Number of Attributes: 5 %% Date Donated: 2008-10-03 %% Associated Tasks: Classification %% Missing Values? N/A %% %% ----------------------------------------------------- %% %% Source: %% %% 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 'at' chu.edu.tw %% TEL:886-3-5186511 %% %% 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%) %% %% %% ----------------------------------------------------- %% %% 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 %% (doi:10.1016/j.eswa.2008.07.018). @relation blood-transfusion-service @attribute V1 numeric @attribute V2 numeric @attribute V3 numeric @attribute V4 numeric @attribute Class {1,2} 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