Implication of association rules employing FP-growth algorithm for knowledge discovery

Hoque, A. H. M. Sajedul; Mondal, Sujit Kumar; Zaman, Tassnim Manami; Barman, Paresh Chandra; Bhuiyan, Md. Al-Amin

doi:10.1109/iccitechn.2011.6164843

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…Eventually, their frequent item set search process between are unique and diverse [17]. Nowadays, the FP-Growth is one of the quickest algorithms among the rules of association [18]. Frequent Pattern Growth is one of the alternative methods to determine the most frequent item set in a data set.…”

Section: Introductionmentioning

confidence: 99%

Automatic Rule Generator via FP-Growth for Eye Diseases Diagnosis

Kurniawan

Nazri

Abdullah

et al. 2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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The conventional approach in developing a rule-based expert system usually applies a tedious, lengthy and costly knowledge acquisition process. The acquisition process is known as the bottleneck in developing an expert system. Furthermore, manual knowledge acquisition can eventually lead to erroneous in decision-making and function ineffective when designing any expert system. Another dilemma among knowledge engineers are handing conflict of interest or high variance of inter and intrapersonal decisions among domain experts during knowledge elicitation stage. The aim of this research is to improve the acquisition of knowledge level using a data mining technique. This paper investigates the effectiveness of an association rule mining technique in generating new rules for an expert system. In this paper, FP-Growth is the machine learning technique that was used in acquiring rules from the eye disease diagnosis records collected from Sumatera Eye Center (SMEC) Hospital in Pekanbaru, Riau, Indonesia. The developed systems are tested with 17 cases. The ophthalmologists inspected the results from automatic rule generator for eye diseases diagnosis. We found that the introduction of FP-Growth association rules into the eye disease knowledge-based systems, able to produce acceptable and promising eye diagnosing results approximately 88% of average accuracy rate. Based on the test results, we can conclude that Conjunctivitis and Presbyopia disease are the most dominant suffering in Indonesia. In conclusion, FP-growth association rules are very potential and capable of becoming an adequate automatic rules generator, but still has plenty of room for improvement in the context of eye disease diagnosing.

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Section: Introductionmentioning

confidence: 99%

Automatic Rule Generator via FP-Growth for Eye Diseases Diagnosis

Kurniawan

Nazri

Abdullah

et al. 2019

Int. J. Adv. Sci. Eng. Inf. Technol.

View full text Add to dashboard Cite

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“…In the produced association rule, antecedent consists of any number of items of frequent itemsets and consequent consists of only one item. This paper explores the FP-growth algorithm on a sample employee database, which is illustrated in table 1 [9]. …”

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confidence: 99%