MINING FUZZY TEMPORAL ITEMSETS WITHIN VARIOUS TIME INTERVALS IN QUANTITATIVE DATASETS

Document Type : Research Paper

Authors

1 Department of Computer Engineering, Center of Excellence on Soft Computing and Intelligent Information Processing, Ferdowsi University of Mashhad, Mashhad, Iran

2 Faculty of Engineering Science, College of Engineering, University of Tehran, Tehran, Iran

Abstract

This research aims at proposing a new method for discovering frequent temporal itemsets in continuous subsets of a dataset with quantitative transactions. It is important to note that although these temporal itemsets may have relatively high \textit{support} or occurrence within particular time intervals, they do not necessarily get similar \textit{support} across the whole dataset, which makes it almost impossible to extract them using the existing traditional algorithms. This paper directly addresses this problem and introduces a new algorithm called Fuzzy Solid Linguistic Itemset Mining (FSLIM) to discover Solid Linguistic Itemsets (SLIs) within a quantitative dataset. SLI is a new concept introduced here as an essential part of the solution presented in this paper. The proposed method consists of two phases. In the first phase, fuzzy set theory is used to transform each quantitative value to a linguistic item; and in the second phase, all SLIs are extracted. Finally, the efficiency of FSLIM is compared in terms of execution time, scalability and the number of frequent patterns with those of two classic approaches on synthetic datasets. The proposed approach is also applied to an actual Mashhad Urban Traffic dataset in order to illustrate FSLIM's ability in discovering the hidden knowledge that could not be extracted by traditional methods.

Keywords

References

[1] R. Agrawal, T. Imieliski and A. Swami, Mining association rules between sets of items in
large databases, In: ACM SIGMOD Rec., ACM, (1993), 207{216.
[2] J. M. Ale and G. H. Rossi, An approach to discovering temporal association rules, In: Proc.
2000 ACM Symp. Appl. Comput.-Vol. 1, ACM, (2000), 294{300.
[3] R. Agrawal and R. Srikant, Others, Fast algorithms for mining association rules, In: Proc
20th Int Conf Very Large Data Bases VLDB, (1994), 487{499.
[4] X. Chen and I. Petrounias, Discovering temporal association rules: Algorithms, language
and system, In: 16th Int. Conf. Data Eng. ICDE, IEEE Computer Society, San Diego, CA,
(2000), 306-306.
[5] C. Y. Chang, M. S. Chen and C. H. Lee, Mining general temporal association rules for items
with di erent exhibition periods, In: Data Min. 2002 ICDM 2003 Proc. 2002 IEEE Int. Conf.
On, IEEE, (2002), 59{66.
[6] C. H. Chen, T. P. Hong and S. B. Lin, Mining fuzzy temporal knowledge from quantitative
transactions, In: Syst. Sci. Eng. ICSSE 2011 Int. Conf. On, IEEE, (2011), 405{409.
[7] J. Han, J. Pei, Y. Yin and R. Mao, Mining frequent patterns without candidate generation:
A frequent-pattern tree approach, Data Min. Knowl. Discov., 8 (2004), 53{87.
[8] T. P. Hong, Y. Y. Wu and S. L. Wang,An e ective mining approach for up-to-date patterns,
Expert Syst. Appl., 36 (2009), 9747{9752.
[9] T. P. Hong, C. S. Kuo and S. C. Chi, Trade-o between computation time and number of rules
for fuzzy mining from quantitative data, Int. J. Uncertain. Fuzziness Knowl.-Based Syst., 9
(2001), 587{604.
[10] J. W. Huang, B. R. Dai and M. S. Chen, Twain: Two-end association miner with precise
frequent exhibition periods, ACM Trans. Knowl. Discov. Data TKDD., 1 (2007), 8.
[11] H. Ishibuchi and T. Yamamoto, Rule weight speci cation in fuzzy rule-based classi cation
systems, Fuzzy Syst. IEEE Trans. On., 13 (2005), 428{435.
[12] G. C. Lan, C. H. Chen, T. P. Hong and S. B. Lin, A fuzzy approach for mining general
temporal association rules in a publication database, In: Hybrid Intell. Syst. HIS 2011 11th
Int. Conf. On, IEEE, (2011), 611{615.
[13] C. H. Lee, M. S. Chen and C. R. Lin, Progressive partition miner: an ecient algorithm for
mining general temporal association rules, Knowl. Data Eng. IEEE Trans. On., 15 (2003),
1004{1017.
[14] W. J. Lee and S. J. Lee, Discovery of fuzzy temporal association rules, Syst. Man Cybern.
Part B Cybern. IEEE Trans. On., 34 (2004), 2330{2342.
[15] W. J. Lee, J. Y. Jiang and S. J. Lee, Mining fuzzy periodic association rules, Data Knowl.
Eng., 65 (2008), 442{462.
[16] Y. Li, P. Ning, X. S. Wang and S. Jajodia, Discovering calendar-based temporal association
rules, Data Knowl. Eng., 44 (2003), 193{218.
[17] C. W. Lin and T. P. Hong, Temporal data mining with up-to-date pattern trees, Expert Syst.
Appl., 38 (2011), 15143{15150.
[18] S. G. Matthews, M. A. Gongora and A. A. Hopgood, Evolving temporal association rules
with genetic algorithms, In: Res. Dev. Intell. Syst. XXVII, Springer, (2011), 107{120.
[19] S. G. Matthews, M. A. Gongora, A. A. Hopgood and S. Ahmadi, Web usage mining with
evolutionary extraction of temporal fuzzy association rules, Knowl.-Based Syst., 54 (2013),
66{72.
[20] S. G. Matthews, M. A. Gongora and A. A. Hopgood, Evolutionary algorithms and fuzzy sets
for discovering temporal rules, Int. J. Appl. Math. Comput. Sci., 23 (2013), 855{868.
[21] S. G. Matthews, M. A. Gongora and A. A. Hopgood, Evolving temporal fuzzy itemsets from
quantitative data with a multi-objective evolutionary algorithm, In: Genet. Evol. Fuzzy Syst.
GEFS 2011 IEEE 5th Int. Workshop On, IEEE, (2011), 9{16.
[22] J. S. Park, M. S. Chen and P. S. Yu, Using a hash-based method with transaction trimming
for mining association rules, Knowl. Data Eng. IEEE Trans. On., 9 (1997), 813{825.
[23] J. Pei, J. Han, H. Lu, S. Nishio, S. Tang and D. Yang, H-mine: Hyper-structure mining of
frequent patterns in large databases, In: Data Min. 2001 ICDM 2001 Proc. IEEE Int. Conf.
On, IEEE, (2001), 441{448.
[24] B. Saleh and F. Masseglia, Discovering frequent behaviors: time is an essential element of
the context, Knowl. Inf. Syst., 28 (2011), 311{331.
[25] S. Suvvari and R. B. V. Subramanyam, An efficient approach for signifi cant time intervals
of frequent itemsets, Int. J. Intell. Syst. Technol. Appl., 13 (2014), 222{243.
[26] Y. Xiao, R. Zhang and I. Kaku, A new framework of mining association rules with time-
windows on real-time transaction database, Int. J. Innov. Comput. Inf. Control., 7 (2011),
3239{3253.
[27] Y. Xiao, Y. Tian and Q. Zhao, Optimizing frequent time-window selection for association
rules mining in a temporal database using a variable neighbourhood search, Comput. Oper.
Res., 52 (2014), 241{250.
[28] J. S. Yoo and S. Shekhar, Similarity-pro led temporal association mining, Knowl. Data Eng.
IEEE Trans. On., 21 (2009), 1147{1161.
[29] L. A. Zadeh, Fuzzy sets, Inf. Control., 8 (1965), 338{353.
[30] C. Zhuo, L. Jiahui and L. Chen, A fuzzy calendar-based algorithm for mining temporal as-
sociation rules and its application, In: Fuzzy Syst. Knowl. Discov. 2009 FSKD09 Sixth Int.
Conf. On, IEEE, (2009), 28{33.
Iranian Journal of Fuzzy Systems
Volume 13, Issue 7
November and December 2016
Pages 67-89

Files

Share

How to cite

Statistics