@article{MAKHILLIJSC20149121180,
    title = {Predominant Pattern Mining using ODIP Technique with Online Time Series Data},
    journal = {International Journal of Soft Computing},
    volume = {9},
    number = {1},
    pages = {44-50},
    year = {2014},
    issn = {1816-9503},
    doi = {ijscomp.2014.44.50},
    url = {https://makhillpublications.co/view-article.php?issn=1816-9503&doi=ijscomp.2014.44.50},
    author = {B. and},
    keywords = {Predominant pattern mining,discrete interested pattern,online time series data,interlaced unwanted data,optimal value,multiplex tree pruning},
    abstract = {Extracting predominant pattern in a time series database is a major data mining 
  problem with several applications. The existing closed sequential patterns permit 
  us to improve efficiency without bringing down the accuracy. The narrative technique 
  developed a previous research follows a multiplex tree pruning technique which 
  combines both the prefix and suffix tree patterns in an activity normalized 
  time periodicity data sequences. The combinatorial point of prefix and suffix 
  trees is on the threshold of predominant data pattern occurrence rate which 
  efficiently identify the regularity of all observed patterns but still obtains 
  the interlaced unwanted data. To separate the interlaced unwanted data from 
  the predominant pattern mining, researchers are going to implement a new technique 
  termed Optimized Discrete Interested Pattern technique (ODIP). This technique 
  identifies the optimal value using the repetition occurrence in the pattern. 
  An analytical and empirical result offers an efficient and effective predominant 
  pattern mining framework for highly dynamic online time series data. Performance 
  of the optimized discrete interested pattern technique is measured in terms 
  of interlaced data removal efficiency, time taken for online pattern mining 
  based on the frequency. Experiments are conducted with online time series data 
  obtained from research repositories of both synthetic and real data sets.}
    }