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A Multilevel Genetic Algorithm for the Maximum Constraint Satisfaction Problem Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Estimating Prevalence Bounds of Temporal Association Patterns to Discover Temporally Similar Patterns

Authors : Vangipuram Radhakrishna, P. V. Kumar, V. Janaki, N. Rajasekhar

Published in: Recent Advances in Soft Computing

Publisher: Springer International Publishing

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Abstract

Mining Temporal Patterns from temporal databases is challenging as it requires handling efficient database scan. A pattern is temporally similar when it satisfies subset constraints. The naive and apriori algorithm designed for non-temporal databases cannot be extended to find similar temporal patterns from temporal databases. The brute force approach requires computing \(2^n\) true support combinations for ‘n’ items from finite item set and falls in NP-class. The apriori or fp-tree based approaches are not directly extendable to temporal databases to obtain similar temporal patterns. In this present research, we come up with novel approach to discover temporal association patterns which are similar for pre-specified subset constraints, and substantially reduce support computations by defining expressions to estimate support bounds. The proposed approach eliminates computational overhead in finding similar temporal patterns. The results prove that the proposed method outperforms brute force approach.

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previous chapter A Computationally Efficient Approach for Mining Similar Temporal Patterns
next chapter An Approach for Imputation of Medical Records Using Novel Similarity Measure
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Metadata
Title
Estimating Prevalence Bounds of Temporal Association Patterns to Discover Temporally Similar Patterns
Authors
Vangipuram Radhakrishna
P. V. Kumar
V. Janaki
N. Rajasekhar
Copyright Year
2017
Book
Recent Advances in Soft Computing

Print ISBN: 978-3-319-58087-6

Electronic ISBN: 978-3-319-58088-3

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-58088-3_20

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