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Using the top-k frequent item set for mining non-overlapping patterns

Gulve, Shivam Yogesh (2023) Using the top-k frequent item set for mining non-overlapping patterns. Masters thesis, Dublin, National College of Ireland.

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Identifying frequent patterns where sub patterns are not repeated is called nonoverlapping maximum sequence pattern (NMSP) mining. Applying non-overlapping pattern discovery mining, a unique kind of repeating sequential pattern mining which uses gap restrictions, it’s indeed possible to discover more useful patterns. Recent techniques focused on discovering common patterns, which resulted in the identification of more compact, repeated patterns. However, it also makes it much more challenging to attempt to identify, necessary information, which has an impact on the effectiveness of mining. We have put forward the efficient modified mining algorithm NetNMSP and will give a set of data, and then in order to identify unique NMSP patterns, it will provide the pattern count to create a sequence. The next step would be to provide top k count i.e., pattern count and pattern size. Basically, there will be a pattern count present and pattern size, so it will look for a range of patterns. Applying the pattern joining approach, which initially apply the joining method to create common patterns, is the third phase. So, at the end, then there will have a candidate pattern for maximal sequential patterns that do not overlap. The proposed system have remove minimum support dependency and this system is efficient based on processing time and processing memory.

Item Type: Thesis (Masters)
Rafai, Hicham
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
Q Science > QA Mathematics > Algebra > Algorithms > Computer algorithms
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Tamara Malone
Date Deposited: 18 May 2023 14:49
Last Modified: 18 May 2023 14:49

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