Shift Window FPTree - An Efficient Stream Mining Algorithm

Full Text (PDF, 473KB), PP.13-20

Views: 0 Downloads: 0

Author(s)

Deepak K Mishra 1,* Varsha Sharma 1

1. School of Information Technology, RGPV, Bhopal MP-462036, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijeme.2015.04.02

Received: 12 May 2015 / Revised: 16 Jun. 2015 / Accepted: 3 Aug. 2015 / Published: 8 Sep. 2015

Index Terms

Data stream mining, flow of data, continues mining

Abstract

Breathless flow in data collection and storage mechanism has enabled Firms to heap up a massive amount of data. In many cases, these huge volumes of data can be mined for fascinating and applicable information in a wide range of applications. When the arrival of data is fast as well in a large bunches in term of amount, this lead major problem to go through this data in both the circumstances in store it and in extracting the useful information from it. To taking under these issues continues mining or stream mining is a best way. Data steam mining allows to not storing the entire data for future prediction which lead to overcome the e-vestige and unnecessary storage overhead. But there is no such way in literature to mine continues data direct, so first one make it feasible accordingly and then mine it. Here in this paper we present an algorithm which handle stream data in very effective manner.

Cite This Paper

Deepak K Mishra, Varsha Sharma,"Shift Window FPTree - An Efficient Stream Mining Algorithm", IJEME, vol.5, no.4, pp.13-20, 2015. DOI: 10.5815/ijeme.2015.04.02

Reference

[1]R. Agrawal, T. Imeilinski, A. Swami, "Database mining: a performance perspective", IEEE Trans. Knowl Data Eng. 5 (6) 914–925(1993).

[2]Charu C. Aggarwal "Data Streams: Models and algorithms", Springer 2007.

[3]Giannella. C, Han. J, Pei. J, et al., "Mining frequent patterns in data streams at multiple time granularities", In: Next Generation Data Mining. Cambridge, Massachusetts: MIT Press, pp. 11-212, 2003.

[4]Han, J.; Pei, J.; and Yin, Y, "Mining frequent patterns without candidate generation", ACM-SIGMOD Int. Conf. Management of Data (SIGMOD'00), 1.12 2000.

[5]J.H. Chang, W.S. Lee, "A sliding window method for finding recently frequent itemsets over online data streams", J. Inf. Sci. Eng. 20 pp. 753–762(2004).

[6]H.-F. Li, S.Y. Lee, mining frequent itemsets over data streams using efficient window sliding techniques, Expert Syst. Appl. 36 pp.-1466–1477 (2009).

[7]Luigi Troiano, Giacomo Scibelli, "Mining frequent itemsets in data streams within a time horizon". Data & Knowledge Engineering 89, pp.21–37, (2014).

[8]K Kiruba, Dr B Rosiline Jeetha, "A Comparative Study on Hierarchical Clustering in Data Mining", International Journal of Engineering Sciences & Research Technology 3(2), pp.656-659, feb 2014. 

[9]Li Tu, Ling Chen "Finding Frequent Items over Data Stream". Journal of Computational Information Systems, 6 (12): 4127- 4134, 2010.

[10]Chris Giannella, Jiawei Han, Jian Pei, Xifeng Yan, Philip S. Yu "FP-stream Mining frequent patterns in data streams at multiple time granularities". 2002.

[11]Jiawei Han, Jian Pei, Yiwen Yin and Runying Mao, "Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach", Data Mining and Knowledge Discovery, 8, 53–87, 2004.

[12]http://www.almaden.ibm.com/software/quest/Resources.

[13]http://www.census.gov/.