Empirical Methodology and Innovative Algorithm for Mining High Utility Itemset on Temporal Data
Rachith Raj1, Ajay Krishnan2, C.V. Prasanna Kumar3
1Rachith Raj*, P.G Student, Department of Computer Science and I.T Amrita School of Arts and Sciences, Kochi, Amrita Vishwa Vidyapeetham, India.
2Ajay Krishnan, P.G Student, Department of Computer Science and I.T Amrita School of Arts and Sciences, Kochi, Amrita Vishwa Vidyapeetham, India.
3C.V. Prasanna Kumar, Assistant Professor , Department of Computer Science and I.T, Amrita School of Arts and Sciences, Kochi
Manuscript received on February 10, 2020. | Revised Manuscript received on February 21, 2020. | Manuscript published on March 10, 2020. | PP: 1607-1609 | Volume-9 Issue-5, March 2020. | Retrieval Number: E2791039520/2020©BEIESP | DOI: 10.35940/ijitee.E2791.039520
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Ever since the humans found the significance of data we are using different methodologies to extricate data patterns in the most useful way to reach at various conclusions based on our needs. The proper utilization of data will leads to meaning full results. Here our focus is to expand the concept of “High utility itemset mining” and also introducing the concept of “timestamp” on it. Here we proposed an effective algorithm to find the best possible data patterns that generate maximum profit within the given “time period” and also proposed a new framework to calculate the “utility support”. Our analysis can be utilized to improve profitability in business and also make better decisions for improving sales in the organization.
Keywords: Data Mining, Frequent Itemset Mining, High utility Itemset Mining, Temporal Data.
Scope of the Article: Data Mining and Warehousing