Most of the approaches for association rule mining focus on the performance of the discovery of the frequent itemsets. They are based on the algorithms that require the transformation of data from one representation to another, and therefore excessively use resources and incur heavy CPU overhead. This chapter proposes a hybrid algorithm that is resource efficient and provides better performance...

In this work we study the mining of top-K frequent closed itemsets, a recently proposed variant of the classical problem of mining frequent closed itemsets where the support threshold is chosen as the maximum value sufficient to guarantee that the itemsets returned in output be at least K. We discuss the effectiveness of parameter K in controlling the output size and develop an efficient algori...

Several algorithms have been introduced for mining frequent itemsets. The recent datasettransformation approach suffers either from the possible increasing in the number of structures that could be produced through the execution of the algorithm or from the problem of the processing time in either projecting or decomposing the datasets. Moreover, the constructed structure cannot be re-used in a...

Most of the approaches for association rule mining focus on the performance of the discovery of the frequent itemsets. They are based on the algorithms that require to transform data from one representation to another, and therefore excessively use resource and incur heavy CPU overhead. This paper proposes a hybrid algorithm that is a resource-efficient and provides better performance. It chara...

Classical frequent itemset mining identifies frequent itemsets in transaction databases using only frequency of item occurrences, without considering utility of items. In many real world situations, utility of itemsets are based upon user’s perspective such as cost, profit or revenue and are of significant importance. Utility mining considers using utility factors in data mining tasks. Utility-...

The mining frequent itemsets plays an important role in the mining of association rules. Frequent itemsets are typically mined from binary databases where each item in a transaction may have a different significance. Mining Frequent Weighed Itemsets (FWI) from weighted items transaction databases addresses this issue. This paper therefore proposes algorithms for the fast mining of FWI from weig...

The set of frequent closed itemsets uniquely determines the exact frequency of all itemsets, yet it can be orders of magnitude smaller than the set of all frequent itemsets. In this paper we present CHARM, an efficient algorithm for mining all frequent closed itemsets. It enumerates closed sets using a dual itemset-tidset search tree, using an efficient hybrid search that skips many levels. It ...

Discovery of association rules is an important problem in KDD process. In this paper we propose a new algorithm for fast frequent itemset mining, which scan the transaction database only once. All the frequent itemsets can be efficiently extracted in a single database pass. To attempt this objective, we define a new compact data structure, called ST-Tree (Signature Transaction Tree), and a new ...

In field of data mining, mining the frequent itemsets from huge amount of data stored in database is an important task. Frequent itemsets leads to formation of association rules. Various methods have been proposed and implemented to improve the efficiency of Apriori algorithm. This paper focuses on comparing the improvements proposed in classical Apriori Algorithm for frequent item set mining. ...

There are many methods which have been developed for improving the time of mining frequent itemsets. However, the time for generating association rules were not put in deep research. In reality, if a database contains many frequent itemsets (from thousands up to millions), the time for generating association rules is more longer than the time for mining frequent itemsets. In this paper, we pres...