A Survey on Mining Frequent Item Sets from Data Stream

A Survey on Mining Frequent Item Sets from Data Stream

Bhargavi PeddireddyAnuradha Ch Sri R. Chandra Murty Patnala1 

Department of Computer Science and Engineering, ANUCET, Acharya Nagarjuna University, Guntur 522510, A.P, India.

Department of Computer Science and Engineering, V.R. Siddhartha Engineering College, Vijayawada 520007, A.P, India

Corresponding Author Email: 
srirampatnala@gmail.com
Page: 
27-30
|
DOI: 
https://doi.org/10.18280/ama_d.230105
Received: 
10 October 2018
|
Accepted: 
5 December 2018
|
Published: 
31 September 2020
| Citation

OPEN ACCESS

Abstract: 

Data mining is a process of finding undisclosed, unknown and interested patterns from databases. It has led to the many techniques, and they emphasis on mining frequent patterns to capture patterns whose occurrence is frequent, unusual and rare occurrence patterns. The research has led to optimize the performance of the techniques with its applications. But, traditional data mining techniques are limited to the databases which exhibits static behavior. But, the real time applications like sensors and stock data exhibits the behavior where the incoming data speed is fast and the cumulated data is huge. Such kind of databases are named as Data streams. The compatibility of data streams with the applications has led to the many issues and challenges. It has been motivated researchers to propose various frameworks and algorithms to speed up the mining process. In this paper, we discuss the various Applications of Data Streams, issues, and challenges. We discuss various models such are Landmark, Sliding window, Damped, and Title timed widow models. And also discuss various frequent itemset mining Algorithms for each models. In addition, this paper also discusses research issues and future direction towards for variety of pattern mining.

Keywords: 

data streams,FPM, data base, itemset

1. Introduction
2.Preliminaries
3. Mining Frequent Patterns from Data Streams
4. Research Issues
5. Future Directions
6. Conclusions
  References

[1] Rakesh A, Srikant RK. (1995). Mining sequential patterns. Data Engineering. Proceedings of the Eleventh International Conference on IEEE, pp. 3-14. http://dx.doi.org/10.1109/ICDE.1995.380415

[2] Saha B, Lazarescu M, Venkatesh S. (2007). Infrequent item mining in multiple data streams. Seventh IEEE International Conference on Data Mining Workshops (ICDMW 2007), pp. 569-574. https://doi.org/10.1109/ICDMW.2007.32

[3] Giannella C, Han J, Pei J, Yan X, Yu PS. (2003). Mining frequent patterns in data streams at multiple time granularities. Next Generation Data Mining 212: 191-212.

[4] Hemalatha CS, Vaidehi V, Lakshmi R. (2015) Minimal infrequent pattern based approach for mining outliers in data streams. Expert Systems with Applications 42(4): 1998--2012. https://doi.org/10.1016/j.eswa.2014.09.053

[5] Huang D, Koh YS, Dobbie G. (2012). Rare pattern mining on data streams. International Conference on Data Warehousing and Knowledge Discovery, LNCS 7448: 303-314. https://doi.org/10.1007/978-3-642-32584-7_25/

[6] Huang DTJ, Koh YS, Dobbie G, Pears R. (2014). Detecting changes in rare patterns from data streams. PAKDD-2014, LNAI 8444, pp. 437-448. http://dx.doi.org/10.1007/978-3-319-06605-9_36

[7] Manku GS, Motwani R. (2002). Approximate frequency counts over data streams. Proceedings of the 28th International Conference on Very Large Data Bases, pp. 346-357. http://dx.doi.org/10.1016/B978-155860869-6/50038-X

[8] Karnati R, Subramanyam RBV. (2016). Efficiently maintaining and discovering sequential patterns with sequence deletion using discovered sequences. International Journal of Applied Engineering Research 11(1): 685-691. https://www.ripublication.com/ijaer16/ijaerv11n1_104.pdf

[9] Deypir M, Sadreddini MH, Hashemi S. (2012). Towards a variable size sliding window model for frequent itemset

mining over data streams. Computers & Industrial Engineering 63(1): 161-172. https://doi.org/10.1016/j.cie.2012.02.008

[10] Jian P, Han JW, Mao RY. (2000). CLOSET: An efficient algorithm for mining frequent closed itemset. ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery 4(2). https://doi.org/10.1109/CSSE.2008.1042

[11] Jin R, Agrawal G. (2007). Frequent pattern mining in data streams. Data Streams: Models and Algorithms. http://dx.doi.org/10.1007/978-0-387-47534-9_4

[12] Tanbeer SK, Ahmed CF, Jeong BS, Lee YK. (2009). Sliding window-based frequent pattern mining over data streams. Information sciences 179(2): 3843--3865. http://dx.doi.org/10.1016/j.ins.2009.07.012

[13] Lee VE, Jin R, Agrawal G. (2014). Frequent pattern mining in data streams. Frequent Pattern Mining, Springer, pp. 199-224. http://dx.doi.org/10.1007/978-3-319-07821-2_9

[14] Chi Y, Wang H, Yu PS, Muntz RR. (2004). Moment: Maintaining closed frequent itemsets over a stream sliding window. Data Mining, ICDM'04. http://dx.doi.org/10.1007/s10115-006-0003-0

[15] Kim YH, Kim WY, Kim UM. (2010). Mining frequent itemsets with normalized weight in continuous data streams. Journal of Information Processing Systems 6(1): 79-90. http://dx.doi.org/10.3745/JIPS.2010.6.1.079