DocumentCode :
166090
Title :
Serial multimethod Combined Mining
Author :
Deshpande, A. ; Mahajan, Aditya
Author_Institution :
Dept. of Comput. Eng., G.H. Raisoni Coll. of Eng., Nagpur, India
fYear :
2014
fDate :
24-27 Sept. 2014
Firstpage :
2631
Lastpage :
2635
Abstract :
Combined Mining is an approach to combine various mining techniques to get more understandable and useful patterns from complex data. Different classical data mining techniques have their advantages and disadvantages so a single technique cannot be applied on different business data. Serial Multi-method Combined Mining (SMCM) is an approach where different mining techniques are used to get the patterns and finally the resultant patterns or rules are actionable. The actionable patterns are descriptive and assist us to finalize business decision. In SMCM, different mining methods are applied in predetermined sequential pattern. The resultant patterns of the previous method are also considered as a part of the input for the next method to be executed. SMCM helps to get advantages of different classical mining techniques to generate combined patterns but it needs the domain knowledge of business data for selection of different methods. The SMCM for credit card data by combining clustering and association techniques is demonstrated and experimental results are taken.
Keywords :
business data processing; data mining; pattern clustering; SMCM; actionable patterns; association techniques; business data; business decision; clustering techniques; complex data; data mining techniques; domain knowledge; predetermined sequential pattern; serial multimethod combined mining; Conferences; Decision support systems; Hafnium; Handheld computers; Informatics; Asssociation Rule Mining; Clustering; Combined pattern mining; Pattern Discovery;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Advances in Computing, Communications and Informatics (ICACCI, 2014 International Conference on
Conference_Location :
New Delhi
Print_ISBN :
978-1-4799-3078-4
Type :
conf
DOI :
10.1109/ICACCI.2014.6968408
Filename :
6968408
Link To Document :
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