We propose a general framework to formalize the problem of capturing the intensity of implication for association rules through statistical metrics. In this framework we present properties that influence the interestingness of a rule, analyze the conditions that lead a measure to perform a perfect prune at a time, and define a final proper order to sort the surviving rules. We will discuss why ...

In this paper, we propose a novel mining task: mining frequent superset from the database of itemsets that is useful in bioinformatics, e-learning systems, jobshop scheduling, and so on. A frequent superset means that it contains more transactions than minimum support threshold. Intuitively, according to the Apriori algorithm, the level-wise discovering starts from 1-itemset, 2itemset, and so f...

Association rule mining is one of the vital data mining tasks to extract knowledge from the data. In the process of association rule mining the foremost step is to find the frequent itemset. The frequent itemset is used to generate association rules. In general brute –force approach is expensive because there are exponentially many rules that can be generated from the data set. So that support ...

-Data Mining can be delineated as an action that analyze the data and draws out some new nontrivial information from the large amount of databases. Traditional data mining methods have focused on finding the statistical correlations between the items that are frequently appearing in the database. High utility itemset mining is an area of research where utility based mining is a descriptive type...

High utility itemsets mining is relevant for business vendors. So that they can give more offers to high utility itemsets. To understand the above sentence we need to know what is high utility itemsets. High utility itemsets are those ones that yield high profit when sold together or alone that meets a user-specified minimum utility threshold from a transactional database. This high utility ite...

Frequent generalized itemset mining is a data mining technique utilized to discover a high-level view of interesting knowledge hidden in the analyzed data. By exploiting a taxonomy, patterns are usually extracted at any level of abstraction. However, some misleading high-level patterns could be included in the mined set. This paper proposes a novel generalized itemset type, namely theMisleading...

1. Summary. In this paper the authors propose a differentially privacy preserving algorithm for mining frequent itemset. This work differs from the other privacy preserving miners present in literature, indeed this algorithm mines the itemset by enforcing cardinality constraints on the transactions present in the dataset. In particular the authors study how the reduction the cardinality of the ...

Frequent itemset mining is an essential part of data analysis and data mining. Recent works propose interesting SAT-based encodings for the problem of discovering frequent itemsets. Our aim in this work is to define strategies for adapting SAT solvers to such encodings in order to improve models enumeration. In this context, we deeply study the effects of restart, branching heuristics and claus...