Constraint Based Periodicity Mining in Time Series Databases

Karthik, G. M.

doi:10.5815/ijcnis.2012.10.04

Cited by 4 publications

(3 citation statements)

References 34 publications

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“…Our previous work reflects the following Science Publications JCS (Pujeri and Karthik, 2012). For DNA, |Σ| is 4 and the symbols are the 20 amino acids.…”

Section: For Mining Periodic Patternmentioning

confidence: 99%

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Frequent Correlated Periodic Pattern Mining for Large Volume Set Using Time Series Data

Karthik¹,

Karthik²

2014

Journal of Computer Science

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Frequent pattern mining has been a widely used in the area of discovering association and correlations among real data sets. However, discovering interesting correlation relationship among huge number of cooccurrence patterns are complicated, a majority of them are superfluous or uninformative. Mining correlations among large pile of useless information is extraordinarily useful in real-time applications. In this study, we propose a technique uses FP-tree for mining frequent correlated in periodic patterns from a transactional database. The analysis of time correlation measure tend to improvise the performance based on real time data sets and the result proves the algorithm efficiency by shifting the data sets to various domain towards time series, its correlation and noise-resilient ratio. This work addresses the time correlation factor achieved with the previous evaluated result of time series sequence of FP tree.

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“…Our previous work reflects the following Science Publications JCS (Pujeri and Karthik, 2012). For DNA, |Σ| is 4 and the symbols are the 20 amino acids.…”

Section: For Mining Periodic Patternmentioning

confidence: 99%

“…We tested our algorithm based on our previous work that gathers information based on time series approach (Pujeri and Karthik, 2012) over a number of data sets. For real data experiments, we used supermarket data which contains sanitized data of timed sales transactions for Wal-Mart stores over a period of 15 months.…”

Section: Experimental Evaluationsmentioning

confidence: 99%

Frequent Correlated Periodic Pattern Mining for Large Volume Set Using Time Series Data

Karthik¹,

Karthik²

2014

Journal of Computer Science

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“…Pujeri and Karthik [17] proposed the constraint-based periodicity mining (CBPM) algorithm that uses frequent pattern growth (FPG) tree in time series databases. For constraint-based association rule mining, the user can specify various types of constraints which include constraints based on knowledge, data, dimension, level, interestingness, and rule.…”

Section: Review Of Related Workmentioning

confidence: 99%

Mining Association Rules in Dengue Gene Sequence with Latent Periodicity

Thangam

Vanniappan

2015

Computational Biology Journal

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The mining of periodic patterns in dengue database is an interesting research problem that can be used for predicting the future evolution of dengue viruses. In this paper, we propose an algorithm called Recurrence Finder (RECFIN) that uses the suffix tree for detecting the periodic patterns of dengue gene sequence. Also, the RECFIN finds the presence of palindrome which indicates the possibilities of formation of proteins. Further, this paper computes the periodicity of nucleic acid and amino acid sequences of any length. The periodicity based association rules are used to diagnose the type of dengue. The time complexity of the proposed algorithm is O(n 2 ). We demonstrate the effectiveness of the proposed approach by comparing the experimental results performed on dengue virus serotypes dataset with NCBI-BLAST algorithm.

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