Add to ORKGInternational audienceWe address here two major challenges presented by dynamic data mining: 1) the stability challenge: we have implemented a rigorous incremental density-based clustering algorithm, independent from any initial conditions and ordering of the data-vectors stream, 2) the cognitive challenge: we have implemented a stringent selection process of association rules between clusters at time t-1 and time t for directly generating the main conclusions about the dynamics of a data-stream. We illustrate these points with an application to a two years and 2600 documents scientific information database
A fundamental problem in text data mining is to extract meaningful structure from document streams ...
The spread of real-time applications has led to a huge amount of data shared between users. This vas...
YesData streams have arisen as a relevant research topic during the past decade. They are real‐time,...
International audienceWe address here two major challenges presented by dynamic data mining: 1) the ...
We address here two major challenges presented by dynamic data mining: 1) the stability challenge: w...
International audienceIn the domain of data-stream clustering, e.g., dynamic text mining as our appl...
We introduce a simple and efficient method for clustering and identifying temporal trends in hyper-l...
A challenge created by the recent development in information technology is that people are often fac...
The file attached to this record is the author's final peer reviewed version.Change is one of the bi...
International audienceData-stream clustering is an ever-expanding subdomain of knowledge extraction....
International audienceData stream clustering provides insights into the under- lying patterns of dat...
AbstractThe scope of this research is to aggregate news contents that exists in data streams. A data...
In this paper, we address exploratory analysis of textual data streams and we propose a bootstrappin...
A key problem within data mining is clustering of data streams. Most existing algorithms for data st...
Clusters in document streams, such as online news articles, can be induced by their textual contents...
A fundamental problem in text data mining is to extract meaningful structure from document streams ...
The spread of real-time applications has led to a huge amount of data shared between users. This vas...
YesData streams have arisen as a relevant research topic during the past decade. They are real‐time,...
International audienceWe address here two major challenges presented by dynamic data mining: 1) the ...
We address here two major challenges presented by dynamic data mining: 1) the stability challenge: w...
International audienceIn the domain of data-stream clustering, e.g., dynamic text mining as our appl...
We introduce a simple and efficient method for clustering and identifying temporal trends in hyper-l...
A challenge created by the recent development in information technology is that people are often fac...
The file attached to this record is the author's final peer reviewed version.Change is one of the bi...
International audienceData-stream clustering is an ever-expanding subdomain of knowledge extraction....
International audienceData stream clustering provides insights into the under- lying patterns of dat...
AbstractThe scope of this research is to aggregate news contents that exists in data streams. A data...
In this paper, we address exploratory analysis of textual data streams and we propose a bootstrappin...
A key problem within data mining is clustering of data streams. Most existing algorithms for data st...
Clusters in document streams, such as online news articles, can be induced by their textual contents...
A fundamental problem in text data mining is to extract meaningful structure from document streams ...
The spread of real-time applications has led to a huge amount of data shared between users. This vas...
YesData streams have arisen as a relevant research topic during the past decade. They are real‐time,...