Efficient mining of salinity and temperature association rules from ARGO data

Abstract

This paper presents an efficient technique for analyzing ARGO ocean data comprising time series of salinity/temperature measurements where informative salinity/temperature patterns are extracted. Most traditional mining techniques focus on finding associations among items within one transaction and are therefore unable to discover rich contextual patterns related to location and time. In order to show the associated salinity/temperature variations among different locations and time intervals, for example, “if the salinity rose from 0.15 psu to 0.25 psu in the area that is in the east–northeast direction and is near Taiwan, then the temperature will rise from 0 °C to 1.2 °C in the area that is in the east–northeast direction and is far away from Taiwan next month”, a quantitative inter-transaction association rules mining algorithm is proposed. The FITI and the PrefixSpan algorithms are adopted to maximize the mining efficiency. The strategy is applied to ocean salinity measurements obtained from the waters surrounding Taiwan. These experimental evaluations show that the proposed algorithm achieves better performance than other inter-transaction association rule mining algorithms.