Multi-source Data Mining for e-Learning
- URL: http://arxiv.org/abs/2009.08791v1
- Date: Thu, 17 Sep 2020 15:39:45 GMT
- Title: Multi-source Data Mining for e-Learning
- Authors: Julie Bu Daher, Armelle Brun and Anne Boyer
- Abstract summary: Pattern mining involves extracting interesting frequent patterns from data.
With the increase in the amount of data, multi-source and heterogeneous data has become an emerging challenge in this domain.
This challenge is the main focus of our work where we propose to mine multi-source data in order to extract interesting frequent patterns.
- Score: 3.8673630752805432
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Data mining is the task of discovering interesting, unexpected or valuable
structures in large datasets and transforming them into an understandable
structure for further use . Different approaches in the domain of data mining
have been proposed, among which pattern mining is the most important one.
Pattern mining mining involves extracting interesting frequent patterns from
data. Pattern mining has grown to be a topic of high interest where it is used
for different purposes, for example, recommendations. Some of the most common
challenges in this domain include reducing the complexity of the process and
avoiding the redundancy within the patterns. So far, pattern mining has mainly
focused on the mining of a single data source. However, with the increase in
the amount of data, in terms of volume, diversity of sources and nature of
data, mining multi-source and heterogeneous data has become an emerging
challenge in this domain. This challenge is the main focus of our work where we
propose to mine multi-source data in order to extract interesting frequent
patterns.
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