scholarly journals A Classification Algorithm: Data Mining and Mathematical Model

2021 ◽  
Vol 2068 (1) ◽  
pp. 012012
Author(s):  
R Cheng ◽  
X Kong ◽  
M Yu ◽  
N Wang
Keyword(s):  
Data Mining ◽  
Mathematical Model ◽  
Classification Algorithm ◽  
Marketing Strategies ◽  
Shopping Mall ◽  
Mining Method ◽  
Data Mining Method ◽  
Rfm Model

Abstract In this paper, we propose a classification algorithm based on Recency-Frequency-Monetary (RFM) model and K-means data mining method. In addition, the designed algorithm is verified by the experiments on the member data in a large shopping mall. The experiments results show that the proposed algorithm can provide an accurate classification of the members. Finally, some marketing strategies for different classes of members are given according to the classification results.

ANALISA POLA PEKERJAAN LULUSAN STMIK BUDI DARMA MENERAPKAN METODE C4.5

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Anisa Anisa ◽  
Mesran Mesran
Keyword(s):  
Data Mining ◽  
Large Scale ◽  
Large Data ◽  
Analysis Data ◽  
Mining Method ◽  
Training Set ◽  
Data Mining Method ◽  
Large Scale Data ◽  
Scale Data

Data mining is mining or discovery information to the process of looking for patterns or information that contains the search trends in a number of very large data in taking decisions on the future.In determining the patterns of classification techniques garnered record (Training set). The class attribute, which is a decision tree with method C 4.5 builds upon an algorithm of induction can be minimised.By utilizing data jobs graduates expected to generate information about interest & talent, work with benefit from graduate quisioner alumni. A pattern of work that sought from large-scale data and analyzed by various algorithms to compute the C 4.5 can do that work based on the pattern of investigation patterns that affect so that it found the rules are interconnected that can result from the results of the classification of objects of different classes or categories of attributes that influence to shape the patterns of work. The application used is software that used Tanagra data mining for academic and research purposes.That contains data mining method explored starting from the data analysis, and classification data mining.Keywords: analysis, Data Mining, method C 4.5, Tanagra, patterns of work

On Predicting the Future Locations of Moving Objects

2016 ◽  
pp. 1899-1917
Author(s):  
Nicola Corona ◽  
Fosca Giannotti ◽  
Anna Monreale ◽  
Roberto Trasarti
Keyword(s):  
Data Mining ◽  
Side Effects ◽  
Mobile Devices ◽  
Moving Objects ◽  
Moving Object ◽  
Location Based Services ◽  
Mining Method ◽  
Data Mining Method ◽  
Mobility Data

The pervasiveness of mobile devices and location-based services produces as side effects an increasing volume of mobility data, which in turn creates the opportunity for a novel generation of analysis methods of movement behaviors. In this chapter, the authors focus on the problem of predicting future locations aimed at predicting with a certain accuracy the next location of a moving object. In particular, they provide a classification of the proposals in the literature addressing that problem. Then the authors preset the data mining method WhereNext and finally discuss possible improvements of that method.

Identification and classification of toe-walkers based on ankle kinematics, using a data-mining method

2006 ◽  
Vol 23 (2) ◽  
pp. 240-248 ◽  
Author(s):  
Stéphane Armand ◽  
Eric Watelain ◽  
Moïse Mercier ◽  
Ghislaine Lensel ◽  
François-Xavier Lepoutre
Keyword(s):  
Data Mining ◽  
Mining Method ◽  
Data Mining Method ◽  
Ankle Kinematics

On Predicting the Future Locations of Moving Objects

2014 ◽  
pp. 30-47
Author(s):  
Nicola Corona ◽  
Fosca Giannotti ◽  
Anna Monreale ◽  
Roberto Trasarti
Keyword(s):  
Data Mining ◽  
Side Effects ◽  
Mobile Devices ◽  
Moving Objects ◽  
Moving Object ◽  
Location Based Services ◽  
Mining Method ◽  
Data Mining Method ◽  
Mobility Data

The pervasiveness of mobile devices and location-based services produces as side effects an increasing volume of mobility data, which in turn creates the opportunity for a novel generation of analysis methods of movement behaviors. In this chapter, the authors focus on the problem of predicting future locations aimed at predicting with a certain accuracy the next location of a moving object. In particular, they provide a classification of the proposals in the literature addressing that problem. Then the authors preset the data mining method WhereNext and finally discuss possible improvements of that method.

Sign in / Sign up

Export Citation Format

Share Document