alexa ON DEMAND EFFICIENT FREQUENT ITEMSET METHOD IN UNCERTAI
ISSN: 1948-1432

Journal of Global Research in Computer Sciences
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Research Article

ON DEMAND EFFICIENT FREQUENT ITEMSET METHOD IN UNCERTAIN DATA

Sanjaydeep Singh Lodhi*1, Sandhya Rawat2 and Premnarayan Arya
  1. Department of Computer Application (Software Systems) S.A.T.I (Govt. Autonomous collage) , Vidisha, (M.P), India [email protected]
  2. Department of C.S.E Truba Engineering College Bhopal, M.P, India [email protected],
  3. Asst. Prof. Dept. of CA (Software Systems) S.A.T.I (Govt. Autonomous collage) , Vidisha, (M.P), India [email protected]
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Abstract

Frequent itemset mining, the task of finding sets of items that frequently occur together in a dataset, has been at the core of the field of data mining for the past sixteen years. In that time, the size of datasets has grown much faster than has the ability of existing algorithms to handle those datasets. Consequently, improvements are needed. In this thesis, we take the classic algorithm for the problem, A Priori, and improve it quite significantly by introducing what we call a vertical sort. We then use the large dataset, web documents to contrast our performance against several state-of-the-art implementations and demonstrate not only equal efficiency with lower memory usage at all support thresholds, but also the ability to mine support thresholds as yet un-attempted in literature. We also indicate how we believe this work can be extended to achieve yet more impressive results.

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