Discovering frequent serial episodes in symbolic sequences for rainfall dataset

Abstract

Serial episode is a type of temporal frequent pattern in time series. Many different algorithms have been proposed to discover different types of episodes for different applications. In this paper we propose an algorithm for discovering frequent episodes from processed rain fall data. The algorithm is based on three main steps. (1) The rainfall data is first represented in symbolic representation (2) Then numbers of events are detected by applying sliding window for segmentation and CBR for classification. (3)Finally the processed rain fall data is passed through mining phase. Frequent algorithm is used to discover frequent episodes with fixed width. The experiment shows that many frequent episodes with different structure in different years are extracted.