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 ...

We propose a novel approach for mining recent frequent itemsets. The approach has three key contributions. First, it is a single-scan algorithm which utilizes the special property of suffix-trees to guarantee that all frequent itemsets are mined. During the phase of itemset growth it is unnecessary to traverse the suffix-trees which are the data structure for storing the summary information of ...

Itemset mining has been an active area of research due to its successful application in various data mining scenarios including finding association rules. Though most of the past work has been on finding frequent itemsets, infrequent itemset mining has demonstrated its utility in web mining, bioinformatics and other fields. In this paper, we propose a new algorithm based on the pattern-growth p...

The class of frequent hypergraph mining problems is introduced which includes the frequent graph mining problem class and contains also the frequent itemset mining problem. We study the computational properties of different problems belonging to this class. In particular, besides negative results, we present practically relevant problems that can be solved in incremental-polynomial time. Some o...

The discovery of frequent itemsets can serve valuable economic and research purposes. Releasing discovered frequent itemsets, however, presents privacy challenges. In this paper, we study the problem of how to perform frequent itemset mining on transaction databases while satisfying differential privacy. We propose an approach, called PrivBasis, which leverages a novel notion called basis sets....

Finding frequent itemsets in a data source is a fundamental operation behind Association Rule Mining. Generally, many algorithms use either the bottom-up or top-down approaches for finding these frequent itemsets. When the length of frequent itemsets to be found is large, the traditional algorithms find all the frequent itemsets from 1-length to n-length, which is a difficult process. This prob...

We describe a frequent itemset mining algorithm and implementation based on the well-known algorithm FPgrowth. The theoretical difference is the main data structure (tree), which is more compact and which we do not need to rebuild for each conditional step. We thoroughly deal with implementation issues, data structures, memory layout, I/O and library functions we use to achieve comparable perfo...

Previous work on frequent itemset mining has focused on finding all itemsets that are frequent in a specified part of a database. In this paper, we motivate the dual question of finding under what circumstances a given itemset satisfies a pattern of interest (e.g., frequency) in a database. Circumstances form a lattice that generalizes the instance lattice associated with datacube. Exploiting t...