Constraint Fuzzy Sequential Pattern Mining with TOPSIS Method
-
3060
Downloads
-
4103
Views
Authors
F. Zabihi
- Industrial Engineering Department, Kharazmi University, Tehran, Iran.
M. M. Pedram
- Computer Engineering Department, Kharazmi University, Tehran, Iran.
M. Ramezan
- Computer Engineering Department, Kharazmi University, Tehran, Iran.
Abstract
To maintain profitability, many companies consider effective customer relationship management
(CRM) to be one of the critical factors for success. The central objective of CRM is to maximize the
lifetime value of a customer to a company and find positive customers. One of the methods that help
this is sequential pattern mining. Sequential pattern mining is to discover all sub-sequences that are
frequent. The classical sequential pattern mining algorithms do not allow processing of numerical
data and require preprocessing of these data into a binary representation, which necessarily leads
to a loss of information. Fuzzy sets are used to overcome this problem. In present fuzzy sequential
pattern mining algorithms, there isn't any matter of itemset time and sequences are only found
based on sequence of happening. In this paper, a novel algorithm about fuzzy sequential pattern
mining is proposed with the time-gap constraints confine the time interval between two adjacent
elements to a reasonable period while the sliding time window constraint permits elements of a pattern to span a set of transactions within a user-specified window and a fuzzy membership function is
considered. Therefore, loss of useful sequences is prevented in the search process. The proposed algorithm searches for a goal sequence within the defined fuzzy sliding window and fuzzy gap functions.
Share and Cite
ISRP Style
F. Zabihi, M. M. Pedram, M. Ramezan, Constraint Fuzzy Sequential Pattern Mining with TOPSIS Method, Journal of Mathematics and Computer Science, 16 (2016), no. 1, 112-130
AMA Style
Zabihi F., Pedram M. M., Ramezan M., Constraint Fuzzy Sequential Pattern Mining with TOPSIS Method. J Math Comput SCI-JM. (2016); 16(1):112-130
Chicago/Turabian Style
Zabihi, F., Pedram, M. M., Ramezan, M.. "Constraint Fuzzy Sequential Pattern Mining with TOPSIS Method." Journal of Mathematics and Computer Science, 16, no. 1 (2016): 112-130
Keywords
- Fuzzy sequential pattern mining
- constraint
- fuzzy gap.
References
-
[1]
R. Agrawal, C. Faloutsos, A. Swami, Efficient similarity search in sequence databases, Lecture Notes in Computer Science, 730 (1993), 69-84.
-
[2]
R. Agrawal, R. Srikant, Mining sequential patterns, Proceedings of 1995 International Conference Data Engineering (ICDE'95), (1995), 3-14.
-
[3]
S. Bringay, A. Laurent, B. Orsetti, P. Salle, M. Teisseire, Handling fuzzy gaps in sequential patterns: Application to health, in FUZZ-IEEE , Korea (2009)
-
[4]
C. Y. Chang, M. S. Chen, C. H. Lee, Mining general temporal association rules for items with different exhibition periods, IEEE International Conference on Data Mining, Maebashi (2002)
-
[5]
C. I. Chang, H. Chueh, N. P. Lin, Sequential patterns mining with fuzzy time-intervals, in IEEE Sixth International Conference on Fuzzy Systems and Knowledge Discovery, (2009)
-
[6]
Y. L. Chen, T. C. K. Huang, Discovering fuzzy time-interval sequential patterns in sequence databases, IEEE transactions on systems, man and cybernetics. Part B, Cybernetics, 35 (2005), 959-972.
-
[7]
R. S. Chen, G. H. Tzeng, C. C. Chen, Y. C. Hu, , Discovery of fuzzy sequential patterns for fuzzy partitions in quantitative attributes, Computer Systems and Applications ACS/IEEE International Conference, (2001), 144-150.
-
[8]
K. Cheng, T. Huang, Iscovery of fuzzy quantitative sequential patterns with multiple minimum supports and adjustable membership functions, Inform. Sci., 222 (2013), 126-146.
-
[9]
C. Faloutsos, M. Ranganathan, Y. Manolopoulos, Fast subsequence matching in time-series databases, Proceedings of the ACM SIGMOD International Conference on Management of Data, Minneapolis (1994)
-
[10]
C. Fiot, A. Laurent, M. Teisseire, Motifs sequentiels flous: un peu, beaucoup, passionnement , e, mesjourneesd'Extraction et Gestion des Connaissances, (2005), 507-518.
-
[11]
C. Fiot, A. Laurent, M. Teisseire, From crispness to fuzziness: Three algorithms for soft sequential pattern mining, IEEE Transactions on Fuzzy Systems, 15 (2007), 1263-1277.
-
[12]
G. Dong, J. Pei, Sequence data mining, Springer Science& Business Media, Maebashi (2007)
-
[13]
M. N. Garofalakis, R. Rastogi, K. Shim, SPIRIT: Sequential pattern mining with regular expression constraints, Proceedings of The 25th International Conference on Very Large Data Bases (VLDB'99), Edinburgh, (1999), 223-234.
-
[14]
M. N. Garofalakis, R. Rastogi, K. Shim , Mining sequential pattern with regular expression constraints, IEEE Trans. Knowl. Data Eng., 14 (2002), 530-552.
-
[15]
F. Guil, A. Bailon, J. Alvarez, R. Marin, Mining generalized temporal patterns based on fuzzy counting , Expert Syst. Appl., 40 (2013), 1296-1304.
-
[16]
J. Han, M. Kamberl, Data mining: concepts and techniques , Academic Press, USA (2001)
-
[17]
J. Han, J. Wang, Y. Lu, P. Tzvetkov, J. Han, Mining top-k frequent closed patterns without minimum support, Proceedings of IEEE International Conference Data Mining, Maebashi, (2002), 221-218.
-
[18]
T. P. Hong, C. S. Kuo, S. C. Chi, Mining fuzzy sequential patterns from quantitative data, Systems, Man and Cybernetics IEEE SMC'99 Conference Proceedings of 1999 IEEE International Conference, (1999), 962-966.
-
[19]
T. P. Hong, K. Y. Lin, S. L. Wang , Mining fuzzy sequential patterns from multiple-item transactions, IFSA World Congress and 20th NAFIPS International Conference, Joint 9th, (2001), 1317-1321.
-
[20]
T. C. K. Huang, Mining the change of customer behavior in fuzzy time-interval sequential patterns, Appl. Soft Comput., 12 (2012), 1068-1086.
-
[21]
M. Kaya, R. Alhaji , multi-objective genetic algorithm-based approach for optimizing fuzzy sequential patterns, Tools with Artificial Intelligence (ICTAI), 16th IEEE International Conference, (2004), 396-400.
-
[22]
G. C. Lan, T. P. Hong, Y. H. Lin, S. L. Wang, Fuzzy utility mining with upper-bound measure, Appl. Soft Comput., 30 (2015), 767-777.
-
[23]
B. Lebaron, S. Weigend, A bootstrap evaluation of the effect of data splitting on financial time series, IEEE Trans. Neural Networks, 9 (1998), 213-220.
-
[24]
C. H. Lee, M. S. Chen, C. R. Lin, Progressive partition miner: an efficient algorithm for mining general temporal association rules , IEEE Trans. Knowl. Data Eng., 15 (2003), 1004-1017.
-
[25]
P. Li, Y. Ning, X. S. Wang, S. Jajodia , Discovering calendar based temporal association rules, in Proceedings of the 8th International Symposium on Temporal Representation and Reasoning, Cividale, (2001), 111-118.
-
[26]
H. Mannila, H. Toivonen, A. I. Verkamo , Discovery of frequent episodes in event sequences, Data Min. Knowl. Discovery, 1 (1997), 259-289.
-
[27]
K. Mehta, S. Bhattacharyya , Adequacy of training data for evolutionary mining of trading rules, Decis. Support Syst., 37 (2004), 461-474.
-
[28]
J. Pei, J. Han, B. Mortazavi-Asl, H. Pinto, Q. Chen, U. Dayal, M. C. Hsu, PrefixSpan: Mining sequential patterns efficiently by prefix-projected pattern growth, Proceedings of International Conference on Data Engineering, Heidelberg, (2001), 215-224.
-
[29]
R. Srikant, R. Agrawal , Mining sequential patterns: generalizations and performance improvements, in Research Report RJ 9994, IBM Almaden Research Center, San Jose, California (1995)
-
[30]
R. Srikant, R. Agrawal, Mining sequential patterns: generalizations and performance improvements, Proceedings of the 5th International Conference on Extending Database Technology, Avignon (1996)
-
[31]
R. Srikant, Y. Yang, Mining web logs to improve website organization, Proceedings of the 10th International World Wide Web Conference, Hong Kong (2001)
-
[32]
J. Srivastava, Mining temporal data, , (http://www.cs.umn.edu/research/websift/survey/. ),
-
[33]
X. Yan, J. Han, CloSpan: mining closed sequential patterns in large datasets, Proceedings of the 2003 SIAM International Conference on Data Mining (SDM'03), San Francisco (2003)
-
[34]
J. Yang, W. Wang, P. Yu, J. Han, Mining long sequential patterns in a noisy environment, Proceedings of the 2002 ACM SIGMOD International Conference on Management of Data, Madison, (2002), 406-417.
-
[35]
F. Zabihi, Fuzzy constrained sequential pattern mining, MSc Thesis, Kharazmi University, Tehran, Iran (2010)
-
[36]
F. Zabihi, M. Ramezan, M. M. Pedram, A. Memariani, Constrained Sequential Pattern Mining, in The 2nd congress on Data Mining, Tehran, Iran (2008)
-
[37]
F. Zabihi, M. Ramezan, M. M. Pedram, A. Memariani, A novel algorithm for fuzzy data mining with sliding window and time gap constrains, in the 2nd joint congress on Fuzzy and Intelligent Syatems, Tehran, Iran (2008)
-
[38]
F. Zabihi, M. Ramezan, M. M. Pedram, A. Memariani, Fuzzy sequential pattern mining with sliding window constraint, in 2nd IEEE International Conference on Education Technology and Computer (ICETC), Shanghai, China (2010)
-
[39]
F. Zabihi, M. Ramezan, M. M. Pedram, A. Memariani, Rule Extraction for Blood donators with fuzzy sequential pattern mining, J. Math. Comput. Sci., 2 (2011), 37-43.
-
[40]
J. M. Zaki, SPADE: An efficient algorithm for mining frequent sequences, Machine Learning, 42 (2001), 31-60.
-
[41]
Q. Zhao, S. S. Bhowmick , Sequential pattern mining: a survey, Technical Report, School of Computer Engineering, Nanyang Technological University, Singapore (2003)