Add to ORKGWe 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 ...
Abstract Analyzing data streams has received considerable attention over the past decades due to the...
We introduce a simple and efficient method for clustering and identifying temporal trends in hyper-l...
We address here two major challenges presented by dynamic data mining: 1) the stability challenge: w...
International audienceWe address here two major challenges presented by dynamic data mining: 1) the ...
International audienceIn the domain of data-stream clustering, e.g., dynamic text mining as our appl...
The file attached to this record is the author's final peer reviewed version.Change is one of the bi...
Due to recent advances in data collection techniques, massive amounts of data are being collected at...
A key problem within data mining is clustering of data streams. Most existing algorithms for data st...
In this paper, we address exploratory analysis of textual data streams and we propose a bootstrappin...
AbstractThe scope of this research is to aggregate news contents that exists in data streams. A data...
YesData streams have arisen as a relevant research topic during the past decade. They are real‐time,...
A challenge created by the recent development in information technology is that people are often fac...
The spread of real-time applications has led to a huge amount of data shared between users. This vas...
International audienceData stream clustering provides insights into the under- lying patterns of dat...
A fundamental problem in text data mining is to extract meaningful structure from document streams ...
Abstract Analyzing data streams has received considerable attention over the past decades due to the...
We introduce a simple and efficient method for clustering and identifying temporal trends in hyper-l...
We address here two major challenges presented by dynamic data mining: 1) the stability challenge: w...
International audienceWe address here two major challenges presented by dynamic data mining: 1) the ...
International audienceIn the domain of data-stream clustering, e.g., dynamic text mining as our appl...
The file attached to this record is the author's final peer reviewed version.Change is one of the bi...
Due to recent advances in data collection techniques, massive amounts of data are being collected at...
A key problem within data mining is clustering of data streams. Most existing algorithms for data st...
In this paper, we address exploratory analysis of textual data streams and we propose a bootstrappin...
AbstractThe scope of this research is to aggregate news contents that exists in data streams. A data...
YesData streams have arisen as a relevant research topic during the past decade. They are real‐time,...
A challenge created by the recent development in information technology is that people are often fac...
The spread of real-time applications has led to a huge amount of data shared between users. This vas...
International audienceData stream clustering provides insights into the under- lying patterns of dat...
A fundamental problem in text data mining is to extract meaningful structure from document streams ...
Abstract Analyzing data streams has received considerable attention over the past decades due to the...
We introduce a simple and efficient method for clustering and identifying temporal trends in hyper-l...