TIME SERIES/SEQUENCE DM
Another important area in DM centers on the mining of time-series and sequence-based data. Simply put, this involves the mining of a sequence of data that can either be referenced by time (time-series, such as stock market and production process data), or is simply a sequence of data that are ordered in a sequence. In general, one aspect of mining time-series data focuses on the goal of identifying movements or components that exist within the data (trend analysis). These can include long-term or trend movements, seasonal variations, cyclical variations, and random movements (Han & Kamber, 2001).
Other techniques that can be used on these kinds of data include similarity search, sequential-pattern mining, and periodicity analysis. Similarity search is concerned with the identification of a pattern sequence that is close or similar to a given pattern. Sequential-pattern mining has as its focus the identification of sequences that occur frequently in a time series or sequence of data. Periodicity analysis attempts to analyze the data from the perspective of identifying patterns that repeat or recur in a time series (Han, Dong, & Yin, 1999; Han & Kamber, 2001; Han, Pei, & Yin, 2000; Kim, Lam, & Han, 2000; Pei, Han, Pinto, Chen, Dayal, & Hsu, 2001; Pei, Tung, & Han, 2001).