Neutrosophic Fuzzy Association Rule Generation-Based Big Data Mining Analysis Algorithm

Abstract

As a very common and classic big data (BD) mining algorithm, the association rule data mining (DM) algorithm is often used to determine the internal correlation between different items and set a certain threshold to determine the size of the correlation. However, the traditional association rule algorithm is more suitable for establishing Boolean association rules between different items of different types of data, and hardening the sharp boundaries of the data causes the performance of the association rules to decrease. In order to overcome this shortcoming of classic DM, this article introduces association rules, support and confidence, the Apriori algorithm and fuzzy association rules based on the neutrosophic fuzzy association rule (NFAR). This paper is based on the data set of the supermarket purchase goods database, by drawing a radar chart to describe the correlation between different goods and different item sets support, and confidence calculation based on association rules support. Finally, the association rules are generated. Compared to the results produced by NFAR and ordinary association rules, the accuracy of the NFAR association rules algorithm in small data sets is 88.48%, while the accuracy of traditional association rules algorithm is only 80.87%, nearly 8 percentage points higher. On large data sets, the prediction accuracy of the neutral fuzzy association rules algorithm is 95.68%, while that of the traditional method is only 89.63%. Therefore, the NFAR algorithm can improve the accuracy and effectiveness of DM. This algorithm has great application prospects and development space in big DM and analysis.

Using a Partition System to Improve the Performance of the Apriori Algorithm in Speeding Up Itemset Frequency Search Process

“…Prinsip metode apriori adalah jika suatu itemset sering muncul (frequent), maka semua subset dari itemset tersebut juga harus sering muncul dalam suatu dataset [1]. Pada algoritma ini calon (k+1)-itemset dihasilkan oleh penggabungan dua itemset pada domain atau ukuran k. Calon (k+1)itemset yang mengandung frekuensi subset yang jarang muncul atau dibawah threshold akan dipangkas dan tidak dipakai dalam menentukan aturan asosiasi [2], [3]. Sesuai dengan aturan asosiasi, algoritma apriori juga menggunakan minimum support dan minimum confidence untuk menentukan aturan itemset, mana yang sesuai untuk digunakan dalam pengambilan keputusan dan menetapkan kebijakan [4].…”

Retracted: Neutrosophic Fuzzy Association Rule Generation-Based Big Data Mining Analysis Algorithm

“…Tis article has been retracted by Hindawi, as publisher, following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of systematic manipulation of the publication and peer-review process.…”