Results 171 to 180 of about 45,837 (241)
Some of the next articles are maybe not open access.
NEclatClosed: A vertical algorithm for mining frequent closed itemsets
Expert systems with applications, 2021Frequent closed itemsets provide a lossless and concise collection of all frequent itemsets to reduce the runtime and memory requirement of frequent itemsets mining tasks.
Nader Aryabarzan, B. Minaei-Bidgoli
semanticscholar +1 more source
A weighted N-list-based method for mining frequent weighted itemsets
Expert Systems With Applications, 2018Huong Bui +4 more
semanticscholar +3 more sources
Using hashing and lexicographic order for Frequent Itemsets Mining on data streams
J. Parallel Distributed Comput., 2019Frequent Itemsets Mining is a Data Mining technique that has been employed to extract useful knowledge from datasets and, more recently, also from data streams.
Lázaro Bustio-Martínez +5 more
semanticscholar +1 more source
Frequent Itemset Mining for Big Data
2013 IEEE International Conference on Big Data, 2013Frequent Itemset Mining (FIM) is one of the most well known techniques to extract knowledge from data. The combinatorial explosion of FIM methods become even more problematic when they are applied to Big Data. Fortunately, recent improvements in the field of parallel programming already provide good tools to tackle this problem.
Moens, Sandy +2 more
openaire +2 more sources
2019
We present a survey of the most important algorithms that have been proposed in the context of the frequent itemset mining. We start with an introduction and overview of basic sequential algorithms, and then discuss and compare different parallel approaches based on shared-memory, message-passing, map-reduce, and the use of GPU accelerators.
Cafaro, Massimo, Pulimeno, Marco
openaire +2 more sources
We present a survey of the most important algorithms that have been proposed in the context of the frequent itemset mining. We start with an introduction and overview of basic sequential algorithms, and then discuss and compare different parallel approaches based on shared-memory, message-passing, map-reduce, and the use of GPU accelerators.
Cafaro, Massimo, Pulimeno, Marco
openaire +2 more sources
Mining top-rank-k frequent weighted itemsets using WN-list structures and an early pruning strategy
Knowledge-Based Systems, 2020Frequent weighted itemsets (FWIs) are a variation of frequent itemsets (FIs) that take into account the different importance or weights for each item. Many algorithms have been introduced for mining FWIs recently.
Bay Vo, Huong Bui, Thanh Vo, Tuong Le
semanticscholar +1 more source
Memory-Aware Frequent k-Itemset Mining
2006In this paper we show that the well known problem of computing frequent k-itemsets (i.e. itemsets of cardinality k) in a given dataset can be reduced to the problem of finding iceberg queries from a stream of queries suitably constructed from the original dataset.
ATZORI M +2 more
openaire +4 more sources
Efficient mining frequent itemsets algorithms
International Journal of Machine Learning and Cybernetics, 2013Efficient algorithms for mining frequent itemsets are crucial for mining association rules as well as for many other data mining tasks. It is well known that countTable is one of the most important facility to employ subsets property for compressing the transaction database to new lower representation of occurrences items. One of the biggest problem in
Marghny H. Mohamed +1 more
openaire +1 more source
Mining Frequent Itemsets from Uncertain Data
2007We study the problem of mining frequent itemsets from uncertain data under a probabilistic framework. We consider transactions whose items are associated with existential probabilities and give a formal definition of frequent patterns under such an uncertain data model.
Kao, B, Chui, CK, Hung, E
openaire +2 more sources
Frequent itemset mining on hadoop
2013 IEEE 9th International Conference on Computational Cybernetics (ICCC), 2013One of the most important problems in data mining is frequent itemset mining. It requires very large computation and I/O traffic capacity. For that reason several parallel and distributed mining algorithms were developed. Recently the mapreduce distributed data processing paradigm is unavoidable and porting the current algorithms to mapreduce is in ...
Ferenc Kovacs, Janos Illes
openaire +1 more source