Maintaining Evidential Frequent Itemsets in Case of Data Deletion

@inproceedings{Tobji2010MaintainingEF,
  title={Maintaining Evidential Frequent Itemsets in Case of Data Deletion},
  author={Mohamed Anis Bach Tobji and Boutheina Ben Yaghlane},
  booktitle={IPMU},
  year={2010}
}
Incremental Maintenance of Frequent Itemsets (IMFI) consists in maintaining a set of extracted patterns when mined data are updated. This field knew considerable improvement in the last decade. However, it is not sufficiently tackled when mined data are imperfect, especially where imperfection is modelled by the evidence theory. In this work, we maintain incrementally the set of initially extracted itemsets both in cases of insertion and deletion of evidential data. Experimentations led on our… 
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References

SHOWING 1-10 OF 16 REFERENCES

Incremental Maintenance of Frequent Itemsets in Evidential Databases

This paper introduces a new maintenance method whose experimentations show efficiency compared to classic methods and tries to resolve the problem of Incremental Maintenance of Frequent Itemsets (IMFI) in the context of evidential data.

A New Algorithm for Mining Frequent Itemsets from Evidential Databases

A new algorithm for mining frequent itemsets from evidential databases called RidLists is proposed that is the vertical representation of the evidential database and adapted to itemsets belief computation which makes the mining algorithm more ecient.

Efficient Mining of Frequent Patterns from Uncertain Data

This work proposes a tree-based mining algorithm to efficiently find frequent patterns from uncertain data, where each item in the transactions is associated with an existential probability.

Maintenance of discovered association rules in large databases: an incremental updating technique

An incremental updating technique is developed for maintenance of the association rules discovered by database mining when new transaction data are added to a transaction database.

Incremental Mining on Association Rules

Previously proposed algorithms for deriving precise association rules efficiently and effectively in dynamic transaction databases and approaches to generate approximations from data streams have received a significant amount of research attention recently.

Query Processing in Temporal Evidential Databases

The temporal evidential database (TED) model, which incorporates time into the evidential relational database model, is presented and it is shown how to integrate the evaluations of both temporal and non-temporal predicates in query processing.

Rule mining and classification in imperfect databases

This work presents a data structure that is an alternate representation of a belief theoretic relational database, and develops efficient algorithms to query for belief of item sets, extract frequent item sets and generate corresponding association rules from this representation.

Mining Association Rules under Imprecision and Vagueness: towards a Possibilistic Approach

A generalized possibilistic relational model is first proposed in this paper and it is proposed to enlarge this framework and consider imprecise and uncertain quantitative data.

"Secure" Logistic Regression of Horizontally and Vertically Partitioned Distributed Databases

This work describes "secure" Newton- Raphson protocol for binary logistic regression in the case of horizontally and vertically partitioned databases using secure-mulity party computation and draws from both PPDM and SDL paradigms.

Fuzzy association rules and the extended mining algorithms