Classification and prediction in temporal data is one of important tasks of data mining which are used in various fields such as financial data forecasting, meteorological data prediction, geographic data processing, and biomedical data analysis. A number of methods have been developed based on learning models such as decision tree, neural network or using other data mining task such as clustering, association rule and sequence pattern mining. One of the classification algorithms based on sequential mining is Classify-by-Sequence (CBS) algorithm. CBS algorithm is a combination of sequential pattern mining with probabilistic induction. However, this algorithm has a drawback regarding its computational time which is quite inefficient. This paper proposes an improvement of CBS algorithm to solve the computational time's inefficiency. The enhancement is conducted by improving the pruning part using FGSP and FEAT algorithm. These algorithms produce classifier that will use to classify testing data. The quality of classifier is then measured using covacct parameter which is the combination of coverage and accuracy. To achieve the purpose of this research, we use meteorological data to classify rain or dry season. The simulation result show that the computational time of modified CBS algorithm with FGSP algorithm is faster than the modification using FEAT algorithm with the same level of accuracy.

Original languageEnglish
Pages (from-to)644-651
Number of pages8
JournalJournal of Theoretical and Applied Information Technology
Issue number3
Publication statusPublished - 1 Sept 2014


  • Classify by sequence
  • FEAT
  • FGSP
  • Pruning strategy
  • Temporal data


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