Volume 37 Issue 5
May  2011
Turn off MathJax
Article Contents
You Yuyang, Zhang Jianpei, Yang Zhihong, et al. Construction of fault-tolerant synopsis over data stream based on prefix-tree[J]. Journal of Beijing University of Aeronautics and Astronautics, 2011, 37(5): 564-568. (in Chinese)
Citation: You Yuyang, Zhang Jianpei, Yang Zhihong, et al. Construction of fault-tolerant synopsis over data stream based on prefix-tree[J]. Journal of Beijing University of Aeronautics and Astronautics, 2011, 37(5): 564-568. (in Chinese)

Construction of fault-tolerant synopsis over data stream based on prefix-tree

  • Received Date: 02 Nov 2010
  • Publish Date: 30 May 2011
  • Complexity of data mining algorithm over data stream is the most important and it should be more focused on algorithm efficiency because of the great consumption of algorithm resources. Fault-tolerant frequent pattern mining is more generalized and suitable for extracting interesting knowledge from real-world data stream polluted by noise. An algorithm, called data stream fault-tolerant frequent pattern tree(DSFT-tree ), was proposed. It could achieve a frequency-descending and highly compact prefix-tree structure with a single-pass to capture fault-tolerant frequent itemsets in recent sliding window. To completely and efficiently perform the tree restructuring operation, an efficient mechanism based on sliding window pointer and bit-vector representation were utilized to restructure the tree. The efficient reconstruction mechanism greatly reduced the consumption of calculation resources and achieved fault-tolerant frequent itemsets mining. Experimental transaction database was generated by IBM synthetic data generator. The number of frequent itemsets extracted by DSFT-tree is 1.5 fold greater than that extracted by FP-stream. Experimental results show that developed algorithm is an efficient fault-tolerant synopsis.

     

  • loading
  • [1] Beringer J,Hullermeier E.Online clustering of parallel data streams[J].Data & Knowledge Engineering,2006,58(2):180- 204 [2] Li H F,Lee S Y.Mining frequent itemsets over data streams using efficient window sliding techniques [J].Expert Systems with Applications,2009,36(2):1466-1477 [3] Chang J H,Lee W S.Online data stream mining of recent frequent itemsets by sliding window method[J].Journal of Information Science,2005,31(2):76-90 [4] Yu J X,Chong Z H,Lu H J,et al.A false negative approach to mining frequent itemsets from high speed transactional data streams[J].Information Sciences,2006,176 (14):1986-2015 [5] Yang C,Fayyad U,Bradley P S.Efficient discovery of error-tolerant frequent itemsets in high dimensions //Proc of 2001 ACM Int Conf on Knowledge Discovery in Databases.San Francisco,CA:Association for Computing Machinery,2001:194-203 [6] Bashir S, Halim Z,Baig A R.Mining fault tolerant frequent patterns using pattern growth approach //AICCSA 08-6th IEEE/ACS International Conference on Computer Systems and Applications.Doha,Qatar:Inst of Elec and Elec Eng Computer Society,2008:172-179 [7] Zhang S C,Zhang J L,Zhang C Q.EDUA:an efficient algorithm for dynamic database mining[J].Information Sciences,2007,177(13):2756-2767 [8] Chi Y,Wang H,Yu P S,et al.Catch the moment:maintaining closed frequent itemsets over a data stream sliding window[J].Knowledge and Information Systems,2006,10(3):265-294
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views(2554) PDF downloads(867) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return