A novel fuzzy similarity measure and prevalence estimation approach for similarity profiled temporal association pattern mining

Data generated from Sensors, IoT environment and many real time applications is mainly spatial, temporal, or spatio-temporal. Some of them include data generated from geospatial, geographical, medical, weather, finance and environmental applications. Such data objects changes over time. Conventional knowledge discovery techniques available do not address the need for analyzing such complex datasets and hence data analysis has become increasingly complex and challenging. Soft computing principles such as fuzzy logic, evolutionary and nature inspired computations may be applied to analyze dynamically varying data. Analyzing temporal trends of association patterns requires handling the temporal data, as prevalence values of temporal patterns are implicitly vectors. Finding Prevalence values of temporal association patterns and validating them for similarity using conventional approach increases the computational complexity. This makes it challenging as the conventional data mining algorithms do not address this need. In this research, we propose a novel approach for estimation of temporal association pattern prevalence values and a novel temporal fuzzy similarity measure which holds monotonicity to find similarity between any two temporal patterns. Experiments are performed considering naive, sequential, spamine and proposed approach. The results obtained show the proposed approach is promising and reduces computational complexity in terms of computing true prevalence and optimizing execution times.