Pattern discovery algorithm for weather prediction problem

This paper proposes two pattern discovery algorithms for weather prediction problem. The episodes mining algorithm is introduced to find frequent episodes in rainfall sequences and sequential pattern mining algorithm to find relationship of patterns between weather stations. Real data are collected from ten rainfall stations of Selangor State, Malaysia. The sequential pattern algorithm is applied to extract the relationship between ten rainfall stations in 33 years periods of time. The proposed study produces valuable patterns of weather and preserves important knowledge for weather prediction.