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Incremental Temporal Mining using Incremental TPMiner and Incremental P-TPMiner Algorithms

by R. V. Argiddi, Sonali Vijaykumar Rampure
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 173 - Number 7
Year of Publication: 2017
Authors: R. V. Argiddi, Sonali Vijaykumar Rampure
10.5120/ijca2017915365

R. V. Argiddi, Sonali Vijaykumar Rampure . Incremental Temporal Mining using Incremental TPMiner and Incremental P-TPMiner Algorithms. International Journal of Computer Applications. 173, 7 ( Sep 2017), 23-27. DOI=10.5120/ijca2017915365

@article{ 10.5120/ijca2017915365,
author = { R. V. Argiddi, Sonali Vijaykumar Rampure },
title = { Incremental Temporal Mining using Incremental TPMiner and Incremental P-TPMiner Algorithms },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2017 },
volume = { 173 },
number = { 7 },
month = { Sep },
year = { 2017 },
issn = { 0975-8887 },
pages = { 23-27 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume173/number7/28348-2017915365/ },
doi = { 10.5120/ijca2017915365 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:20:38.528451+05:30
%A R. V. Argiddi
%A Sonali Vijaykumar Rampure
%T Incremental Temporal Mining using Incremental TPMiner and Incremental P-TPMiner Algorithms
%J International Journal of Computer Applications
%@ 0975-8887
%V 173
%N 7
%P 23-27
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Temporal data mining is one type of "predictive”. Temporal Association mining is sequential mining. We usually predict what will happen next or what is probability that certain thing happen Sequential pattern mining is one important case of data mining. Most of sequential pattern mining algorithm works on static data which deals with the database should not change. But the databases in real world application do not have static data rather they have incremental database. There are some applications using temporal event data have used to discovering patterns from events. There are two types of interval-based patterns: Temporal pattern and Probabilistic temporal pattern are proposed. This paper attempts to provide two algorithms Incremental Temporal Pattern Miner (TP-Miner) and Probabilistic Temporal Pattern Miner (P-TP Miner).In this project, apply proposed algorithms to real datasets to make the comparison of Incremental temporal mining and Non-incremental temporal mining.

References
  1. Yi-Cheng Chen, Wen-Chih Peng and Suh-Yin Lee, “Mining Temporal Patterns in Time Interval Based Data” IEEE Transactions on Knowledge and Data Engineering, 1041-4347 (c) 2015 IEEE.
  2. Chen,C.Chen,W.Peng and W. Lee, “Mining Correlation Patterns among Appliances in Smart Home Environment,” IEEE 18th Pacific-Asia Conference in Knowledge Discovery and Data Mining, Advances in Knowledge Discovery and Data Mining (PAKDD’14), pp. 210-221, 2014.
  3. J.Kolter, and M. Johnson, “REDD: A public data set for energy disaggregation research,” KDD workshop on Data Mining Applications in Sustainability (SustKDD’11), pp. 1-6, 2011.
  4. S. van Schaik, D. Olteanu and R. Fink, “ENFrame: A Platform for Processing Probabilistic Data,” The 17th International Conference on Extending Database Technology (EDBT’14), pp. 355-366,2014.
  5. Y. Li, J. Bailey, L. Kulik and J. Pei, “Mining Probabilistic Frequent Spatio-Temporal Sequential Patterns with Gap Constraints from Uncertain Databases,” The 13th International Conference on Data Mining (ICDM’13), pp. 448-457, 2013.
  6. R.Sadasivam and K. Duraiswamy, “Efficient Method to Discover Interval-based Sequential Patterns,” Journal of Computer Science, vol. 9, issue 2, pp. 225-234, 2013.
  7. A.Wong, D. Zhuang, G. Li, and E. Lee, “Discovery of Closed Patterns and Noninduced Patterns from Sequences,” IEEE Transactions on Knowledge and Data Engineering, vol.24, no. 8, pp.1408-1421, 2012.
  8. A.Zakour, S. Maabout, M. Mosbah and M. Sistiaga, “Uncertainty Interval Temporal Sequences Extraction,” International Conference on Information Systems Technology and Management (ICISTM’ 12), pp. 259-270, 2012.
  9. Z. Zhao, D. Yan and W. Ng, “Mining Probabilistically Frequent Sequential Patterns in Large Uncertain Databases,” The 15th International Conference on Extending Database Technology (EDBT’12), pp. 74-85, 2012.
  10. H. Kim, M. Marwah, M. Arlitt, G. Lyon and J. Han, “Unsupervised disaggregation of low frequency power measurements,” The 11th SIAM International Conference on Data Mining (SDM’11),pp. 747–758, 2011.
  11. M. Muzammal and R. Raman, “Mining Sequential Patterns from Probabilistic Databases,” The 15th Pacific-Asia Conference in Knowledge Discovery and Data Mining, Advances in Knowledge Discovery and Data Mining, (PAKDD’11), pp. 210-221, 2011.
  12. Yi-Cheng Chen, Ji-Chiang Jiang, Wen-Chih Peng and Suh-Yin Lee,”An Efficient Algorithm for Mining Time Interval-based Patterns in Large Databases”,(CIKM’10), October 26–30, 2010 2010 ACM.
Index Terms

Computer Science
Information Sciences

Keywords

Sequential pattern Incremental Temporal Pattern Interval based pattern Data mining.

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