Add to ORKGMany data streaming applications produces massive amounts of data that must be processed in a distributed fashion due to the resource limitation of a single machine. We propose a distributed data stream clustering protocol. Theoretical analysis shows preliminary results about the quality of discovered clustering. In addition, we present results about the ability to reduce the time complexity respect to the centralized approach
Abstract Identifying clusters is an important aspect of analyzing large datasets. Clustering algorit...
International audienceWe introduce a novel algorithm to perform graph clustering in the edge streami...
Clustering data stream is an active research area that has recently emerged to discover knowledge fr...
A widely used approach to clustering a single data stream is the two-phased approach in which the on...
Abstract—Extraction of patterns out of streaming data that are generated from geographically dispers...
Data is often collected over a distributed network, but in many cases, is so voluminous that it is i...
Extraction of patterns out of streaming data that are generated from geographically dispersed device...
As data gathering grows easier, and as researchers discover new ways to interpret data, streaming-da...
Abstract Analyzing data streams has received considerable attention over the past decades due to the...
In data stream clustering, it is desirable to have algorithms that are able to detect clusters of ar...
Clustering algorithms are an important tool for data mining and data analysis purposes. Clustering a...
In this Final Master Project, a Machine Learning algorithm for clustering named CluStream was applie...
Identifying clusters is an important aspect of analyzing large datasets. Clustering algorithms class...
K-means clustering plays a vital role in data mining. As an iterative computation, its performance w...
Abstract — Continuous clustering analysis over a data stream reports clustering results incrementall...
Abstract Identifying clusters is an important aspect of analyzing large datasets. Clustering algorit...
International audienceWe introduce a novel algorithm to perform graph clustering in the edge streami...
Clustering data stream is an active research area that has recently emerged to discover knowledge fr...
A widely used approach to clustering a single data stream is the two-phased approach in which the on...
Abstract—Extraction of patterns out of streaming data that are generated from geographically dispers...
Data is often collected over a distributed network, but in many cases, is so voluminous that it is i...
Extraction of patterns out of streaming data that are generated from geographically dispersed device...
As data gathering grows easier, and as researchers discover new ways to interpret data, streaming-da...
Abstract Analyzing data streams has received considerable attention over the past decades due to the...
In data stream clustering, it is desirable to have algorithms that are able to detect clusters of ar...
Clustering algorithms are an important tool for data mining and data analysis purposes. Clustering a...
In this Final Master Project, a Machine Learning algorithm for clustering named CluStream was applie...
Identifying clusters is an important aspect of analyzing large datasets. Clustering algorithms class...
K-means clustering plays a vital role in data mining. As an iterative computation, its performance w...
Abstract — Continuous clustering analysis over a data stream reports clustering results incrementall...
Abstract Identifying clusters is an important aspect of analyzing large datasets. Clustering algorit...
International audienceWe introduce a novel algorithm to perform graph clustering in the edge streami...
Clustering data stream is an active research area that has recently emerged to discover knowledge fr...