Nouvelles reprΓ©sentations concises exactes des motifs rares
April 02, 2020 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Seif Ben Chaabene
arXiv ID
2004.07123
Category
cs.IR: Information Retrieval
Cross-listed
cs.DL
Citations
0
Venue
arXiv.org
Last Checked
4 months ago
Abstract
Until a present, the majority of work in data mining were interested in the extraction of the frequent itemsets and the generation of the frequent association rules from these itemsets. Sometimes, the frequent of associations rules can revealed not-interesting in the direction where a frequent behavior is in general a normal behavior in the database. These last years, some work was focused on the exploitation and the extraction of rare itemset and shows them interest. However, the very important size of those itemset was the handicap of algorithms that exploit the rare pattern. In order to relieve this problem, the present report proposes two exact concise representations of the rare itemset, one based on the minimal generators and the other based on the closed itemset. In this context, we introduce two new algorithms called GMRare and MFRare which extract these two exact concise representations.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Information Retrieval
R.I.P.
π»
Ghosted
π
π
Old Age
Neural Graph Collaborative Filtering
R.I.P.
π»
Ghosted
DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
R.I.P.
π»
Ghosted
BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer
R.I.P.
π
404 Not Found
Graph Neural Networks for Social Recommendation
R.I.P.
π»
Ghosted
Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted