Krishna Sudarsana—A Z-Space Interest Measure for Mining Similarity Profiled Temporal Association Patterns View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-03-02

AUTHORS

Radhakrishna Vangipuram, P. V. Kumar, Vinjamuri Janaki, Shadi. A. Aljawarneh, Juan A. Lara, Khalaf Khatatneh

ABSTRACT

Similarity profiled association mining from time stamped transaction databases is an important topic of research relatively less addressed in the field of temporal data mining. Mining temporal patterns from these time series databases requires choosing and applying similarity measure for similarity computations and subsequently pruning temporal patterns. This research proposes a novel z-space based interest measure named as Krishna Sudarsana for time-stamped transaction databases by extending interest measure Srihass proposed in previous research. Krishna Sudarsana is designed by using the product based fuzzy Gaussian membership function and performs similarity computations in z-space to determine the similarity degree between any two temporal patterns. The interest measure is designed by considering z-values between z = 0 and z = 3.09. Applying the Krishna Sudarsana requires moving the threshold value given by user to a different transformation space (z-space) which is a defined as a function of standard deviation. In addition to proposing interest measure, new expressions for standard deviation and equivalent z-space threshold are derived for similarity computations. For experimental evaluation, we considered Naïve, Sequential and Spamine algorithms that applies Euclidean distance function and compared performance of these three approaches to Z-Spamine algorithm that uses Krishna Sudarsana by choosing various test cases. Experiment results proved the performance of the proposed approach is better to Sequential approach that uses snapshot database scan strategy and Spamine approach that uses lattice based database scan strategy. More... »

PAGES

1-22

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10699-019-09590-y

DOI

http://dx.doi.org/10.1007/s10699-019-09590-y

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1112504247


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