We propose a new algorithm for searching frequent itemsets in large data bases. The idea is to start searching from a set of representative examples instead of testing the 1-itemset,the k-itemset and so on. A clustering algorithm is firstly applied in order to cluster the transactions into k clusters. The set of the k representative examples will be used as the starting point for searching freq...

Real world datasets are sparse, dirty and contain hundreds of items. In such situations, discovering interesting rules (results) using traditional frequent itemset mining approach by specifying a user defined input support threshold is not appropriate. Since without any domain knowledge, setting support threshold small or large can output nothing or a large number of redundant uninteresting res...

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...

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...

In general frequent itemsets are generated from large data sets by applying various association rule mining algorithms, these produce many redundant frequent itemsets. In this paper we proposed a new framework for Non-redundant frequent itemset generation using closed frequent itemsets without lose of information on Taxonomy Datasets using concept lattices. General Terms Frequent Pattern, Assoc...

Mining of frequent patterns is a basic problem in data mining applications. Frequent Itemset Mining is considered to be an important research oriented task in data mining, due to its large applicability in real world applications. In this paper, a new Maximal Frequent Itemset mining algorithm with effective pruning mechanism is proposed. The proposed algorithm takes vertical tidset representati...

The purpose of this work is to mine closed frequent itemsets from transactional data streams using a sliding window model. An efficient algorithm IMCFI is proposed for Incremental Mining of Closed Frequent Itemsets from a transactional data stream. The proposed algorithm IMCFI uses a data structure called INdexed Tree(INT) similar to NewCET used in NewMoment[5]. INT contains an index table Item...

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...