Zulkurnain, Nurul Fariza and Shah, Ahmad (2018) HYBRID: an efficient unifying process to mine frequent itemsets. In: 2017 IEEE 3rd International Conference on Engineering Technologies and Social Sciences (ICETSS), 7th-8th August 2017, Bangkok, Thailand.
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Abstract
Current advancement in technology inexorably leads to data flood. More data is generated from banking, telecom, scientific experiments, etc. Data mining is the process of extracting useful information from this flooded data, which helps in making profitable future decisions in these fields. Frequent itemset mining is one of the focus research areas and an important step to fin association rules. Time and space requirements for generating frequent itemsets are of utter importance. Algorithms to mine frequent itemsets effectively help in finding association rules and also help in many other data mining tasks. In this paper, an efficient hybrid algorithm was designed using a unifying process of the algorithms Improved Apriori and FP-Growth. Results indicate that the proposed hybrid algorithm, albeit more complex, consumes fewer memory resources and faster execution time.
| Item Type: | Conference or Workshop Item (Plenary Papers) |
|---|---|
| Additional Information: | 4123/65091 |
| Uncontrolled Keywords: | data mining; frequent itemset; association rule; big data. |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices > TK7885 Computer engineering |
| Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): | Kulliyyah of Engineering Kulliyyah of Engineering > Department of Electrical and Computer Engineering |
| Depositing User: | DR Nurul Fariza Zulkurnain |
| Date Deposited: | 15 Aug 2018 16:50 |
| Last Modified: | 17 Sep 2018 00:46 |
| URI: | http://irep.iium.edu.my/id/eprint/65091 |
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