"In recent years the analysis of data streams has received a lot of attention.. This is motivated by the increase of the number of applications which generate huge. amounts of high speed temporal data. Let us think to sensor networks, computer networks,. manufactures. Data streams are usually highly evolving, thus mining changes. in data is a challenging task. In this paper we will deal with the structural drift detection. problem where the aim is to discover and to describe changes in proximity. relations among multiple data streams. We will introduce a new strategy whose effectiveness. is shown through an application on simulated data.
This paper presents an efficient algorithm for detecting changes (drifts) in the utility distributio...
Recently, several approaches have been proposed to deal with the increasingly challenging task of mi...
153 p.Applications that generate data in the form of fast streams from non-stationary environments, ...
The detection of concept drift allows to point out when a data stream changes its behavior over time...
Abstract. In applications such as fraud and intrusion detection, it is of great interest to measure ...
In this paper, we introduce SPIRIT (Stream-ing Pattern dIscoveRy in multIple Time-series). Given n n...
The training set consists of many features that influence the classifier in different degrees. Choos...
In many cases, databases are in constant evolution, new data is arriving continuously. Data streams ...
Stream analysis is considered as a crucial component of strategic control over a broad variety of di...
This paper addresses the challenges in detecting the potential cor-relation between numerical data s...
Emerging real life applications, such as environmental compliance, ecological studies and meteorolog...
Data growth in today’s world is exponential, many applications generate huge amount of data st...
Data collected over time often exhibit changes in distribution, or concept drift, caused by changes ...
"In recent years, Data Stream Mining (DSM) has received a lot of attention due to the increasing num...
Process mining is an emerging data mining task of gathering valuable knowledge out of the huge colle...
This paper presents an efficient algorithm for detecting changes (drifts) in the utility distributio...
Recently, several approaches have been proposed to deal with the increasingly challenging task of mi...
153 p.Applications that generate data in the form of fast streams from non-stationary environments, ...
The detection of concept drift allows to point out when a data stream changes its behavior over time...
Abstract. In applications such as fraud and intrusion detection, it is of great interest to measure ...
In this paper, we introduce SPIRIT (Stream-ing Pattern dIscoveRy in multIple Time-series). Given n n...
The training set consists of many features that influence the classifier in different degrees. Choos...
In many cases, databases are in constant evolution, new data is arriving continuously. Data streams ...
Stream analysis is considered as a crucial component of strategic control over a broad variety of di...
This paper addresses the challenges in detecting the potential cor-relation between numerical data s...
Emerging real life applications, such as environmental compliance, ecological studies and meteorolog...
Data growth in today’s world is exponential, many applications generate huge amount of data st...
Data collected over time often exhibit changes in distribution, or concept drift, caused by changes ...
"In recent years, Data Stream Mining (DSM) has received a lot of attention due to the increasing num...
Process mining is an emerging data mining task of gathering valuable knowledge out of the huge colle...
This paper presents an efficient algorithm for detecting changes (drifts) in the utility distributio...
Recently, several approaches have been proposed to deal with the increasingly challenging task of mi...
153 p.Applications that generate data in the form of fast streams from non-stationary environments, ...