Vinjamuri Janaki

TL;DR: 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 are proposed.

TL;DR: This research addresses the similarity measure for revealing similar temporal patterns from time series data generated by IoT and addresses the problem of seasonal patterns, emerging or diminishing patterns.

TL;DR: A novel dissimilarity measure whose design is a function of product based gaussian membership function through extending the similarity function proposed in earlier research (G-Spamine) is proposed and the correctness and completeness of proposed approach is also proved analytically.

TL;DR: The objective is to propose a dissimilarity measure for detecting outliers, and expression for deviation is come up with which is used to compute Dissimilarity degree using proposed measure.

TL;DR: A novel similarity (or dissimilarity) measure, SRIHASS is introduced to find the similarity between temporal associations, and the algorithm for time profiled associating mining called Z-SPAMINE is given that is primarily inspired from SPAMINE.