In this paper, we first focus our attention on the question of how much space remains for performance improvement over current association rule mining algorithms. Our strategy is...
We present a depth-first algorithm, PatriciaMine, that discovers all frequent itemsets in a dataset, for a given support threshold. The algorithm is main-memory based and employs...
This paper presents the implementation of kDCI, an enhancement of DCI [10], a scalable algorithm for discovering frequent sets in large databases. The main contribution of kDCI re...
Salvatore Orlando, Claudio Lucchese, Paolo Palmeri...
We describe a method for computing closed sets with data-dependent constraints. Especially, we show how the method can be adapted to find frequent closed sets in a given data set...
We will discuss , the depth first implementation of APRIORI as devised in 1999 (see [8]). Given a database, this algorithm builds a trie in memory that contains all frequent item...
A simple new algorithm is suggested for frequent itemset mining, using item probabilities as the basis for generating candidates. The method first finds all the frequent items, an...
We present a new algorithm for mining frequent itemsets. Past studies have proposed various algorithms and techniques for improving the efficiency of the mining task. We integrate...
We present a performance study of the MAFIA algorithm for mining maximal frequent itemsets from a transactional database. In a thorough experimental analysis, we isolate the effec...
Douglas Burdick, Manuel Calimlim, Jason Flannick, ...
The efficiency of frequent itemset mining algorithms is determined mainly by three factors: the way candidates are generated, the data structure that is used and the implementati...
Data mining is the process of deriving knowledge from data. The architecture of a data mining system plays a significant role in the efficiency with which data is mined. It is pro...