Efficient discovery of frequent itemsets in large datasets is a key component of many data mining tasks. In-core algorithms---which operate entirely in main ...
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Jul 8, 2018 · The main objective of this work is to propose a memory efficient method to store the data processed by the frequent itemset mining algorithm.
Efficient discovery of frequent itemsets in large datasets is a key component of many data mining tasks. In-core algorithms—which operate entirely in main ...
Efficient discovery of frequent itemsets in large datasets is a key component of many data mining tasks. In-core algorithms—which operate entirely in main ...
We present a strategy for mining frequent itemsets from terabyte-scale data sets on cluster systems. The algorithm embraces the holistic notion of architecture- ...
A new approach is proposed in which transactions are represented in a compact graph with the number of nodes equal to thenumber of distinct items in a ...
In this paper, we focus on memory use in frequent itemset mining. We propose a new approach in which transactions are represented in a compact graph with the ...
Mar 12, 2024 · This paper proposes a novel approach to frequent itemset mining for dense datasets. This approach, after the initial stage, does not use transactional data.
In this paper, we focus on memory use in frequent itemset mining. We propose a new approach in which transactions are represented in a compact graph with the ...
This paper introduces a new way which is more efficient in time and space frequent itemset mining. Our method scans the database only one time whereas the ...