2 research outputs found
Characterization and extraction of condensed representation of correlated patterns based on formal concept analysis
Correlated pattern mining has increasingly become an important task in data
mining since these patterns allow conveying knowledge about meaningful and
surprising relations among data. Frequent correlated patterns were thoroughly
studied in the literature. In this thesis, we propose to benefit from both
frequent correlated as well as rare correlated patterns according to the bond
correlation measure. We propose to extract a subset without information loss of
the sets of frequent correlated and of rare correlated patterns, this subset is
called ``Condensed Representation``. In this regard, we are based on the
notions derived from the Formal Concept Analysis FCA, specifically the
equivalence classes associated to a closure operator fbond dedicated to the
bond measure, to introduce new concise representations of both frequent
correlated and rare correlated patterns
Replication in Data Grids: Metrics and Strategies
We focus in this report on two main axes. The first is dedicated to the study
of the effect of replicas distribution on data grid performances. In this
respect, our main contributions are as follows: 1) An overview of replication
strategies mainly from the viewpoints of the considered parameters in their
associated steps as well as the used metrics in the literature for their
evaluation. 2) A study of the impact of placement strategies on data grid
performance which motivated the analysis of the effect of the replicas
distribution quality on the performance results of replication strategies. 3)
The proposal of new evaluation metrics dedicated to the evaluation of the
distribution quality. 4) The setting of an objective evaluation of replication
strategies which is based on a beforehand assessment of the distribution
quality.
The second axis is mainly dedicated to exploiting results of data mining
techniques to enhance performances of replication strategies. With respect to
this axis, we mainly concentrate on the following contributions listed below:
1) The study of the strengths and the drawbacks of the main replication
strategies based on data mining techniques and how these latter are applied in
this context. 2) The proposal of a new guideline to data mining application in
the context of data grid replication strategies. 3) The proposal of a new
algorithm for mining maximal frequent correlated patterns. The input of this
algorithm is obtained through a preliminary step focusing on how to adapt the
required grid concepts to the data mining algorithm. 4) The design and the
implementation of a new replication strategy based on a data mining technique,
and more precisely correlated patterns