Data evolution analysis describes and models regularities or trends for objects whose behavior variations over time. Although this may involve discrimination, association, classification, characterization, or clustering of time-related data, distinct features of such an analysis involve time-series data analysis, periodicity pattern matching, and similarity-based data analysis.
In the analysis of time-related data, it is often required not only to model the general evolutionary trend of the data but also to identify data deviations that occur over time. Deviations are differences between measured values and corresponding references such as previous values or normative values. A data mining system performing deviation analysis, upon the detection of a set of deviations, may do the following: describe the characteristics of the deviations, try to describe the reason behindhand them, and suggest actions to bring the deviated values back to their expected values.
