@article{MAKHILLIJSC20149121179,
    title = {Multiplex Tree Pruning for Periodic Pattern Mining},
    journal = {International Journal of Soft Computing},
    volume = {9},
    number = {1},
    pages = {37-43},
    year = {2014},
    issn = {1816-9503},
    doi = {ijscomp.2014.37.43},
    url = {https://makhillpublications.co/view-article.php?issn=1816-9503&doi=ijscomp.2014.37.43},
    author = {B. and},
    keywords = {Data mining,sequential pattern mining,periodic pattern mining,multiplex tree pruning technique,suffix tree,prefix tree},
    abstract = {Discovering specified patterns in a time series database has expected much 
  consideration and is nowadays a comparatively mature field. Existing Periodic 
  Pattern Mining algorithms concentrates on mining which involves subsequences. 
  However, huge portion of requests for example, genetic DNA and protein pattern 
  mining requires estimated patterns that are adjacent in nature. The existing 
  algorithms applied to discover such estimated pattern mining comprises of complicated 
  problems such as deprived scalability and complexity while applying towards 
  certain other applications. To overcome these limitations, a novel technique 
  is presented that evolves a set of periodic pattern if the regularity of the 
  occurrence changes from that estimated pattern. The technique is based on the 
  combination of both suffix and prefix tree patterns, to develop a multiplex 
  tree pruning, for an activity normalized time periodicity data sequences. The 
  integrative sequence of prefix and suffix trees is based on the threshold factor 
  of predominant data pattern occurrence rate. The conceptual model of multiplex 
  tree pruning technique presented in this study, in combination with the prefix 
  and suffix tree model for pruning items identifies the regularity of all observed 
  patterns in an efficient manner. The detailed experimental study shows strong 
  gains in periodic pattern mining, ensure fast storage of all the time series 
  for a specified item. Empirical studies with varied time series data obtained 
  from bank and car data set using UCI repositories is measured and evaluated 
  in terms of time efficiency of pruning patterns of interest, sensitivity and 
  accuracy.}
    }