In this study, we propose a new method to enhance the accuracy of Modified Multi-class Classification based on Association Rule (MMCAR) classifier. We introduce a Partial Rule Match Filtering (PRMF) method that allows a minimal match of the items in the rule’s body in order for the rule to be added into a classifier. Experiments on Reuters-21578 data sets are performed in order to evaluate the ...

The concept of Privacy-Preserving has recently been proposed in response to the concerns of preserving personal or sensible information derived from data mining algorithms. For example, through data mining, sensible information such as private information or patterns may be inferred from non-sensible information or unclassified data. As large repositories of data contain confidential rules that...

In this paper, we propose a reconstruction–based approach to classification rule hiding in categorical datasets. The proposed methodology modifies transactions supporting both sensitive and nonsensitive classification rules in the original dataset and then uses the supporting transactions of the nonsensitive rules to produce its sanitized counterpart. To further investigate some interesting pro...

In this paper, the relation among factors in the road transportation sector from March, 2005 to March, 2011 is analyzed. Most of the previous studies have economical point of view on gasoline consumption. Here, a new approach is proposed in which different data mining techniques are used to extract meaningful relations between the aforementioned factors. The main and dependent factor is gasolin...

The study validated the association of organizational justice, and knowledge hiding with moderating role self-monitoring in cross-cultural environment. empirical validity was searched tested from banking sector Pakistan by collecting data through research questionnaires which evaluated that justice; hiding, are associated one another. Non-probability sampling technique used due to limitations g...

This project [4] centers on regional association rule mining and scoping in spatial datasets. We introduces a methodology for mining spatial association rules and proposes new algorithms to determine the scope of a spatial association rule. We develop a reward-based region discovery framework that employs clustering to find interesting regions. The framework is applied to solve two distinct reg...

Association rule mining is the process of finding some relations among the attributes/attribute values of huge database based on support value. Most existing association mining techniques are developed to generate frequent rules based on frequent itemsets generated on market basket datasets. A common property of these techniques is that they extract frequent itemsets and prune the infrequent it...

Association Rule Mining (ARM) is concerned with how items in a transactional database are grouped together. It is commonly known as market basket analysis, because it can be likened to the analysis of items that are frequently put together in a basket by shoppers in a market. From a statistical point of view, it is a semiautomatic technique to discover correlations among a set of variables. ARM...

Mining association rules is a popular and well researched method for discovering interesting relations between variables in large databases. A practical problem is that at medium to low support values often a large number of frequent itemsets and an even larger number of association rules are found in a database. A widely used approach is to gradually increase minimum support and minimum confid...