Sequence classification is an important task in data mining. We address the problem of sequence classification using rules composed of interesting itemsets found in a dataset of labelled sequences and accompanying class labels. We measure the interestingness of an itemset in a given class of sequences by combining the cohesion and the support of the itemset. We use the discovered itemsets to ge...

We proposed a new algorithm SuffixMiner which eliminates the requirement of multiple passes through the data when finding out all frequent itemsets in data streams, takes full advantage of the special property of suffixtree to avoid generating candidate itemsets and traversing each suffix-tree during the itemset growth, and utilizes a new itemset growth method to mine all frequent itemsets in d...

In this paper, we propose a memory efficient algorithm for maximal frequent itemset mining from transactional datasets. We propose OP-MAX* (Order Preserving – MAXimal itemset mining) algorithm, which mines all the maximal itemsets from transactional datasets with less space and time. Our methodology uses a memory efficient maximality checking technique to generate frequent maximal itemsets. We ...

Discovering significant itemsets is one of the fundamental tasks in data mining. It has recently been shown that constraint programming is a flexible way to tackle data mining tasks. With a constraint programming approach, we can easily express and efficiently answer queries with user’s constraints on itemsets. However, in many practical cases queries also involve user’s constraints on the data...

The volume of stored data increases rapidly. Therefore, the battery of extracted association heavily prohibits the better support of the decision maker. In this context, backboned on the Formal Concept Analysis, we propose to extend the notion of Formal Concept through the generalization of the notion of itemset aiming to consider the itemset as an intent, its support as the cardinality of the ...

The amount of data speedily proliferates. Consequently, the excessive number of extracted association greatly prohibits to better assist the decision maker. In this respect, backboned on the Formal Concept Analysis, we propose to extend the notion of Formal Concept through the generalization of the notion of itemset aiming to consider the itemset as an intent, its support as the cardinality of ...

Frequent sets play an important role in many Data Mining tasks that try to search interesting patterns from databases, such as association rules, sequences, correlations, episodes, classifiers and clusters. FrequentItemsets Mining (FIM) is the most well-known techniques to extract knowledge from dataset. In this paper differential privacy aims to get means to increase the accuracy of queries fr...

Private itemset support counting (PISC) is a basic building block of various privacy-preserving data mining algorithms. Briefly, in PISC, Client wants to know the support of her itemset in Server’s database with the usual privacy guarantees. First, we show that if the number of attributes is small, then a communication-efficient PISC protocol can be constructed from a communication-efficient ob...

Itemsets, which are treated as intermediate results in association mining, have attracted significant research due to the inherent complexity of their generation. However, there is currently little literature focusing upon the interactions between itemsets, the nature of which may potentially contain valuable information. This paper presents a novel tree-based approach to discovering itemset in...