V
Vangipuram Radhakrishna
Researcher at VNR Vignana Jyothi Institute of Engineering and Technology
Publications - 68
Citations - 2145
Vangipuram Radhakrishna is an academic researcher from VNR Vignana Jyothi Institute of Engineering and Technology. The author has contributed to research in topics: Temporal database & Similarity measure. The author has an hindex of 29, co-authored 67 publications receiving 2013 citations. Previous affiliations of Vangipuram Radhakrishna include Kakatiya Institute of Technology and Science.
Papers
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Journal ArticleDOI
G-spamine
TL;DR: The objective is to find temporal patterns whose true prevalence values vary similar to a reference support time sequence satisfying subset constraints through estimating temporal pattern support bounds and using a novel fuzzy dissimilarity measure, named G-SPAMINE.
Journal ArticleDOI
A novel fuzzy gaussian-based dissimilarity measure for discovering similarity temporal association patterns
Vangipuram Radhakrishna,Shadi Aljawarneh,P. V. Kumar,Kim-Kwang Raymond Choo,Kim-Kwang Raymond Choo +4 more
TL;DR: A novel approach to retrieve temporal association patterns whose prevalence values are similar to those of the user specified reference, and uses monotonicity property to prune temporal patterns without computing unnecessary true supports and distances.
Journal ArticleDOI
A novel fuzzy similarity measure and prevalence estimation approach for similarity profiled temporal association pattern mining
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.
Proceedings ArticleDOI
A similarity measure for temporal pattern discovery in time series data generated by IoT
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.
Journal ArticleDOI
ASTRA - A Novel interest measure for unearthing latent temporal associations and trends through extending basic gaussian membership function
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.