The Discovery of association rules is a non-supervised task of data mining. Its mostly hard step is to look for the frequent itemsets embedded into large amounts of data. Based on the theory of Formal Concept Analysis, we suggest that the notion of Formal Concept generalizes the notion of itemset, since it takes into account the itemset (as the intent) and the support (as the cardinality of the...

In this paper, we describe a new framework for breaking symmetries in itemset mining problems. Symmetries are permutations between items that leave invariant the transaction database. Such kind of structural knowledge induces a partition of the search space into equivalent classes of symmetrical itemsets. Our proposed framework aims to reduce the search space of possible interesting itemsets by...

Frequent itemset mining is a task that can in turn be used for other purposes such as associative rule mining. One problem is that the data may be sensitive, and its owner may refuse to give it for analysis in plaintext. There exist many privacy-preserving solutions for frequent itemset mining, but in any case enhancing the privacy inevitably spoils the efficiency. Leaking some less sensitive i...

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....

purchase or non-purchase of one item or itemset on the purchase or non-purchase of another item or itemset. The research in this paper models for incorporating purchase dependencies in retail multi-item inventory management. One illustrative example has been discussed with data mining in retail sale data. Various types of purchase dependencies including negative dependency have been identified ...

The organization, management and accessing of information in better manner in various data warehouse applications have been active areas of research for many researchers for more than last two decades. The work presented in this paper is motivated from their work and inspired to reduce complexity involved in data mining from data warehouse. A new algorithm named VS_Apriori is introduced as the ...

We propose an online partial counting algorithm based on statistical inference that approximates itemset frequencies from data streams. The space complexity of our algorithm is proportional to the number of frequent itemsets in the stream at any time. Furthermore, the longer an itemset is frequent the closer is the approximation to its frequency, implying that the results become more precise as...

This paper addresses the problem of finding a class of representative itemsets up to subitemset isomorphism. An efficient algorithm is of practical importance in the domain of optimal sorting networks. Although only exponential algorithms for solving the problem exist in the literature, the complexity classification of the problem has never been addressed. In this paper, we present a complexity...