An Adjunct Quick Mining in Closed Persistent Patterns (AQCPP)
Journal Title: IOSR Journals (IOSR Journal of Computer Engineering) - Year 2013, Vol 8, Issue 5
Abstract
Closed persistent patterns in data mining is biggest challenge in these days. The existing models find these patterns from traditional and transactional databases. This paper approach a novel model to mine closed persistent patterns using the invert matrix for any given traditional data file or for any raw sequential data and adjunct closed persistent element matrix which reduces the consumption of number of iterations or search space for retrieving various persistent patterns of deferent elements. We approach adjunct quick mining in closed persistent patterns (AQCPP) matrix to reduce the iterate levels i.e. the l and l+1 levels in mining conditions. Iteration time improvement is the clear output over the ancestors work in this process. Our experiments resulted efficient quick mining process throughout our analysis the algorithms performed efficiently with less computations. As a case study we also implemented our inline mechanism over Service Patterns to analyze the performance of the proposed AQCPP algorithm over lightweight directories and our results are satisfactory
Authors and Affiliations
K. V Subbaraju
Keywords
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- EP ID EP93398
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