databases and reflect the importance of different items and transaction so as to extract more valuable information, an improved Apriori algorithm is proposed in this paper, which is to build the 0-1 transaction matrix by scanning transaction database for getting the weighted support and confidence....
To maximize training programs and improve individual performance, the sports fitness profession is always looking for new and innovative solutions. Fuzzy d
These data are then extracted by the association rule mining component in order to detect interesting association rules based on support and confidence measures (Le and Lo, 2015). The Apriori algorithm (Li et al., 2016) is used in the association rule mining component for identifying the ...
Criticism to Support and Confidence X and Y: positively correlated, X and Z, negatively related support and confidence of X=>Z dominates We need a measure of dependent or correlated events P(B|A)/P(B) is also called the lift of rule A => B Other Interestingness Measures: Interest Intere...
The Apriori algorithm, applied to each cluster's data (refer to Fig. 9), uncovers hidden patterns crucial for identifying students with satisfactory or unsatisfactory academic performance in M.Sc. studies. Effective rule selection involves carefully setting support and confidence thresholds, impacting ...
The most important problem is how to select the appropriate minimum support and confidence to find frequent itemsets. Apriori algorithm, the widely adopted approach, exploits the following property to derive the frequent itemset: If an itemset is frequent, so are all its subsets. That is, A...
Existing algorithms for association rule mining, such as the Apriori algorithm, cannot be used efficiently for support-less association rule mining since those algorithms mostly rely on identifying frequent item-sets with high support. In this paper, we propose a new model to perform support-less ...
This results in the difficulty of finding correlation rules with low support but high correlation. In this paper a new algorithm called MNI is introduced to use the lower bound of Phi correlation coefficient to generate all candidate negative correlation items and reduce explosive search space. Both...
with the incorporation of fuzzy set theory. Fuzzy association rule mining is applied using the Apriori algorithm to items sold and the amount sold. The approach will allow the e-commerce company to present relevant products to customers based on historical transaction data. Summary of related ...
However, due to the explosive number of itemsets, it is prohibitively expensive to retrieve lots of itemsets before we identify the characteristics of the itemset support distribution in targeted data. As such, we also propose a valid and cost-effective algorithm, called algorithm PPL, to extract...