A Study on Classification Method of Discrete Data Basic on Improved Association Rules

Author(s):  
YueQin Cao
2007 ◽  
Vol 25 (3) ◽  
pp. 235-245
Author(s):  
Takashi Washio ◽  
Koutarou Nakanishi ◽  
Hiroshi Motoda

Author(s):  
Kotaro Nakanishi ◽  
Takashi Washio ◽  
Yuki Mitsunaga ◽  
Atsushi Fujimoto ◽  
Hiroshi Motoda

2021 ◽  
Vol 292 ◽  
pp. 02026
Author(s):  
Song HaiYan ◽  
Zhang Huan

For some companies, sales means arranging goods and selling them on the shelves. This kind of arrangement of goods regardless of priority brings a lot of internal consumption to the enterprise. With the rapid development of the Internet and industry, many large companies have gradually established and improved their production and sales processes. The traditional ABC classification method can no longer meet this demand. The new method that can compensate for the traditional ABC classification method is particularly important. This article uses association rules to use the ABC classification method to study the expected budget of commodities, uses the Apriori and FP-Growth algorithms in the association rules to collect frequent itemsets of the acquired data, calculates the corresponding association rules, and then classifies the commodities with ABC.


2007 ◽  
Vol 44 (02) ◽  
pp. 393-408 ◽  
Author(s):  
Allan Sly

Multifractional Brownian motion is a Gaussian process which has changing scaling properties generated by varying the local Hölder exponent. We show that multifractional Brownian motion is very sensitive to changes in the selected Hölder exponent and has extreme changes in magnitude. We suggest an alternative stochastic process, called integrated fractional white noise, which retains the important local properties but avoids the undesirable oscillations in magnitude. We also show how the Hölder exponent can be estimated locally from discrete data in this model.


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