MMIR: Mining Multi-scale Intervention Rules in Sub-Complex System

Intervention analysis is the common method to reveal relationships between objects in human as well as biological society. Data mining research community is just starting to pay attention to intervention analysis. As the traditional association rules are not successful at measuring intervention, this paper tries to mining intervention rules from time series data of sub-complex system. The main contributions of this study include: (1) introduces a new concept of intervention rules. It can quantitatively detect at what scale, how intensive and how long the intervention does make sense; (2) conduct wavelet transform on time series. The decomposition scale can denotes the intervention scale; (3) proposes a new concept named directional correlation to measure intervention intensity; (4) calculates the intervention intensity between time series data with different time delays. The time delay can uncover after how long the intervention takes place; (5) conducts experiments on real datasets. The results show that intervention rules do exist at different decomposition scale of the original data. Moreover, the number of rules discovered by multi-scale analyzing methods is always 4 or 5 times more than those found by single scale methods. And the stability of rules discovered by the improved multi-scale mining algorithm MMIR* is always 100%, while the stability of rules discovered by the basic multi-scale mining algorithm MMIR fluctuate around 70%.