Using Minimum Bounding Cube to Discover Valuable Salinity/Temperature Patterns from Ocean Science Data

A novel data mining techniques for finding interesting spatial-temporal patterns in ocean data is presented. The data consist of time series measurements of upper ocean science variables (e.g., salinity and temperature). Extracting interesting patterns from ocean variables is of important for understanding the relationship between ocean salinity/ temperature structures and climate variability. Association rules mining is applied in the search for these spatial-temporal patterns. Most traditional data mining models focus on mining association rules among attributes within one transaction. For example, if salinity rose, then temperature rises. However, people may be interested in discovering additional relations among transactions and take context such as time or location into consideration. An example of such a rule might be "if the salinity in area A rose from 5% to 7%, then the temperature in area B will rise from 0% to 2.5% in the next month." In this case, the associated salinity/temperature variations among different locations and days are revealed. To overcome these issues, a multi-dimensional inter-transaction association rules mining framework was developed. Unlike other mining algorithms that suffer from a large number of inter-transaction items, the proposed Apriori-like method treats each event from the ocean science data as a transaction and applies the MBC (minimum bounding cube) to form inter-transactions within the maxspan. Since there is no need to slide the maxspan window, the proposed method is easy to implement and it is computationally efficient.