I. J. of Electrical & Electronics Research
Research Article | 
Auto-Threshold Dynamic Memory Efficient Frequent Pattern Growth for Data Excavation
Author(s): G. Gunasekaran1, S. Murugan2 and K. Mani3
Published In : International Journal of Electrical and Electronics Research (IJEER) Volume 10, Issue 3, Special Issue on IEEE-SD
Publisher : FOREX Publication
Published : 15 September 2022
e-ISSN : 2347-470X
Page(s) : 614-619
Abstract
Discovering patterns from large datasets is inevitable in the modern data driven civilization. Many research works, and business models are depending on this data excavation task. An efficient method for identifying and categorizing different data patterns from an exponentially growing database is required to perform a clear data excavation. A set of fresh processes such as Repeat Pattern Finder, Repeat Pattern Table, Repeat Pattern Threshold Analyzer, and Repeat Pattern Node are conceptualized in this work named as Auto-Threshold Dynamic Memory Efficient Frequent Pattern Growth for Data Excavation (AT-DME-FP). The main motive of this work is to improve the Accuracy, Precision, Recall, and F-Score along with the decrease in time and memory consumption. AT-DME-FP is contrived in a way to reduce the consumption of computational resources to match the modern data mining outgrowth. The memory reduction ability of AT-DME-FP makes it possible to use it with big data seamlessly.
Keywords: Auto-Threshold, Big data, Data Mining, FP-Growth, FP-Tree, Repeat Patterns, Repeat Pattern Table, Repeat Pattern Node
G. Gunasekaran, Research scholar, PG & Research Department of Computer Science, Nehru memorial College, Puthanampatti-621007, Thiruchirappalli-Dt, Tamil Nadu, India; Email: gunasekarangphd@gmail.com
S. Murugan, Associate Professor, PG & Research, Department of Computer Science, Nehru memorial College, Puthanampatti-621007, Thiruchirappalli-Dt, Tamil Nadu, India; Email: murugan_nmc@hotmail.com
K. Mani, Associate Professor, PG & Research, Department of Computer Science, Nehru memorial College, Puthanampatti-621007, Thiruchirappalli-Dt, Tamil Nadu, India; Email: nitishmanil@gmail.com
-
[1] Mendes, R., & Vilela, J. P. (2017). Privacy-preserving data mining: Methods, metrics and applications. In IEEE Access. IEEE Publications, 5, 10562–10582. [Cross Ref]
-
[2] Kavakiotis, I., Tsave, O., Salifoglou, A., Maglaveras, N., Vlahavas, I., & Chouvarda, I. (2017). Machine learning and data mining methods in diabetes research. In Computational and Structural Biotechnology Journal. Elsevier, 15, 104–116.[Cross Ref]
-
[3] Dotson, J. P., Fan, R. R., Feit, E. M., Oldham, J. D., & Yeh, Y. (2017). Brand attitudes and search engine queries. In Journal of Interactive Marketing. Elsevier, 37, 105–116. [Cross Ref]
-
[4] Lu, H., Chen, J., Yan, K., Jin, Q., Xue, Y., & Gao, Z. (2017). A hybrid feature selection algorithm for gene expression data classification. Neurocomputing. Elsevier, 256, 56–62.[Cross Ref]
-
[5] Tarek, S., Abd Elwahab, R. A., & Shoman, M. (2017). Gene expression based cancer classification. In Egyptian Informatics Journal. Elsevier, 18(3), 151–159.[Cross Ref]
-
[6] Han, X., Liu, X., Chen, J., Lai, G., Gao, H., & Li, J. (2019). Efficiently mining frequent itemsets on massive data. In IEEE Access. IEEE Publications, 7, 31409–31421. [Cross Ref]
-
[7] Liu, S., & Pan, H. (2018). Rare itemsets mining algorithm based on RP-Tree and spark framework. In AIP Conference Proceedings. Australian Institute of Physics, 1967(1), 1–8.[Cross Ref]
-
[8] Rezaul Karim, Md., Cochez, M., & Beyan, O. D. (2018). Chowdhury Farhan Ahmed amd Stefan Decker, "Mining maximal frequent patterns in transactional databases and dynamic data streams: A spark-based approach" in Information SciencesVolume 432 (pp. 278–300). Elsevier.[Cross Ref]
-
[9] Resheff, Y. S., & Shahar, M. A statistical approach to inferring business locations based on purchase behavior. In IEEE International Conference on Big Data (Big Data), IEEE 2018 (pp. 2295–2303).[Cross Ref]
-
[10] http://fimi.ua.ac.be/data/kosarak.dat[Cross Ref]
-
[11] https://archive.ics.uci.edu/ml/machine-learning-databases/mushroom/agaricus-lepiota.names[Cross Ref]
-
[12] https://archive.ics.uci.edu/ml/machine-learning-databases/mushroom/[Cross Ref]
-
[13] http://fimi.ua.ac.be/data/pumsb_star.dat[Cross Ref]
-
[14] http://fimi.ua.ac.be/data/retail.dat[Cross Ref]
-
[15] http://fimi.ua.ac.be/data/T10I4D100K.dat [Cross Ref]
-
[16] Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). ‘Data Mining: Practical Machine Learning Tools and Techniques’, Morgan Kaufmann publishers, MK (pp. 1–654). [Cross Ref]
G. Gunasekaran, S. Murugan and K. Mani (2022), Auto-Threshold Dynamic Memory Efficient Frequent Pattern Growth for Data Excavation. IJEER 10(3), 614-619. DOI: 10.37391/IJEER.100333.