In order to achieve efficient processing of complex multidimensional data generated by power grid operation and improve the intelligence level of power grid operation, this paper studies the multi-attribute data class...
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Objective:According to RFM model theory of customer relationship management,data mining technology was used to group the chronic infectious disease patients to explore the effect of customer segmentation on the manage...
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Objective:According to RFM model theory of customer relationship management,data mining technology was used to group the chronic infectious disease patients to explore the effect of customer segmentation on the management of patients with different ***:170,246 outpatient data was extracted from the hospital management information system(HIS) during January 2016 to July 2016,43,448 data was formed after the data cleaning.K-Means clustering algorithm was used to classify patients with chronic infectious diseases,and then c5.0 decision tree algorithm was used to predict the situation of patients with chronic infectious ***:Male patients accounted for 58.7%,patients living in Shanghai accounted for 85.6%.The average age of patients is 45.88 years old,the high incidence age is 25 to 65 years *** was gathered into three categories:1) clusters 1—Important patients(4786 people,11.72%,R = 2.89,F = 11.72,M = 84,302.95);2) clustering 2—Major patients(23,103,53.2%,R = 5.22,F = 3.45,M = 9146.39);3) cluster 3—Potential patients(15,559 people,35.8%,R = 19.77,F = 1.55,M = 1739.09).c5.0 decision tree algorithm was used to predict the treatment situation of patients with chronic infectious diseases,the final treatment time(weeks) is an important predictor,the accuracy rate is 99.94% verified by the confusion ***:Medical institutions should strengthen the adherence education for patients with chronic infectious diseases,establish the chronic infectious diseases and customer relationship management database,take the initiative to help them improve treatment *** governments at all levels should speed up the construction of hospital information,establish the chronic infectious disease database,strengthen the blocking of mother-to-child transmission,to effectively curb chronic infectious diseases,reduce disease burden and mortality.
Auctions have been a popular way of transaction on the Internet. Most of the studies of auction assume participants attending the auction are homogeneous. However, this assumption is open to question. In fact, every p...
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Auctions have been a popular way of transaction on the Internet. Most of the studies of auction assume participants attending the auction are homogeneous. However, this assumption is open to question. In fact, every participant has his own personality, risk attitude, behavior, and cost when attending online auctions. This study takes an empirical approach and uses four variables, time of entry, time of exit, number of bids, and number of jump bids, to find the heterogeneity among bidders. We first used k-means clustering method to identify the types of bidders of online auctions, and then used c5.0decisiontree learning algorithm to find the rules to differentiate bidders. A taxonomy of four types of bidders is proposed in the study, which include observers, adventurers, opportunists, and early players. The results also suggest pacing of the auctions is an important factor that will affect bidder's behavior in online auctions.
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