TY  - JOUR
T1  - Predominant Pattern Distribution Model for Noise Distributed Time Series Database
AU - Sujatha, B. AU - Pandian, S. Chenthur 
JO  - International Journal of Soft Computing
VL  - 8
IS  - 4
SP  - 276
EP  - 282
PY  - 2013
DA  - 2001/08/19
SN  - 1816-9503
DO  - ijscomp.2013.276.282
UR  - https://makhillpublications.co/view-article.php?doi=ijscomp.2013.276.282
KW  - Time series
KW  -periodic pattern mining
KW  -periodicity types
KW  -suffix tree
KW  -Predominant Pattern Distribution Model
AB  - A time series is collection of well-defined data sets obtained 
  through repeated measurements of time. Extraction of periodic pattern in a time 
  series database is significant one in data mining problem that predicts and 
  forecasts the future behavior of the data at regular time interval. Periodic 
  pattern mining involves several applications such as prediction, forecasting, 
  detection of unusual activities. The difficulty is not trivial because the data 
  to be examined are regularly noisy and diverse periodicity types (that is symbol, 
  sequence and segment) are to be examined. The whole time series or in a subsection 
  of it to effectively handle various types of noise (to a definite degree) and 
  at the same time to detect different types of periodic patterns. The existing 
  suffix tree based periodic pattern mining algorithm can detect symbol, sequence 
  and segment periodicity in time series data with noise filters for diverse noise 
  kinds. But the running time desired to identify the patterns without redundancy 
  is high. So, to overcome this issue, in this study, Predominant Pattern Distribution 
  Model is introduced with which redundant and unwanted noisy patterns are identified 
  and discarded from the time series data. Predominant patterns are extracted 
  with automatic or user defined threshold of pattern of interest, generated from 
  the dynamic online time series data. Experiments conducted on both synthetic 
  and real data sets of research repositories including protein sequences. Performance 
  of proposed framework is measured and evaluated in terms of periodic pattern 
  mining accuracy, noise distribution rate and predominant pattern occurrence.
ER  -