In recent years curve clustering problem has been handled in several applicative fields. However, most of the proposed approaches are sensitive to outliers. This paper aims to deal with this problem in order to make a partition, obtained by using a Dynamic Curve Clustering Algorithm with free knots spline estimation, more robust. The approach is based on a leave-some-out strategy, which defines a rule on the distances distribution of the curves from the barycenters, in order to identify outliers regions. The method is validated by an application on real data
Mahalanobis-type distances in which the shape matrix is derived from a consistent highbreakdown robu...
Abstract: In modern era there are lots of data mining algorithms which focus on clustering methods. ...
In this paper we examine some of the relationships between two important optimization problems that ...
In recent years curve clustering problem has been handled in several applicative fields. However, mo...
"A two-phase clustering method for the detection of geostatistical functional. outliers is proposed....
Outliers are eccentric data points with anomalous nature. Clustering with outliers has received a lo...
[[abstract]]In this paper, a two-phase clustering algorithm for outliers detection is proposed. We f...
Outlier detection is a fundamental issue in data mining, specifically it has been used to detect and...
Abstract An outlier elimination algorithm for curve/surface fitting is proposed. This two-stage hybr...
We examine relationships between the problem of robust estimation of multivariate location and shape...
Abstract—In this paper a novel Support vector clustering(SVC) method for outlier detection is propos...
[[abstract]]Identifying outliers an remainder clusters which are used to designate few patterns that...
Romano E., Giordano G., Lauro C. N. (2006), "An inter-models distance for clustering utility functio...
Outlier detection is an important problem occurring in a wide range of areas. Outliers are the outco...
The problem of curve clustering when curves are misaligned is considered. A novel algorithm is descr...
Mahalanobis-type distances in which the shape matrix is derived from a consistent highbreakdown robu...
Abstract: In modern era there are lots of data mining algorithms which focus on clustering methods. ...
In this paper we examine some of the relationships between two important optimization problems that ...
In recent years curve clustering problem has been handled in several applicative fields. However, mo...
"A two-phase clustering method for the detection of geostatistical functional. outliers is proposed....
Outliers are eccentric data points with anomalous nature. Clustering with outliers has received a lo...
[[abstract]]In this paper, a two-phase clustering algorithm for outliers detection is proposed. We f...
Outlier detection is a fundamental issue in data mining, specifically it has been used to detect and...
Abstract An outlier elimination algorithm for curve/surface fitting is proposed. This two-stage hybr...
We examine relationships between the problem of robust estimation of multivariate location and shape...
Abstract—In this paper a novel Support vector clustering(SVC) method for outlier detection is propos...
[[abstract]]Identifying outliers an remainder clusters which are used to designate few patterns that...
Romano E., Giordano G., Lauro C. N. (2006), "An inter-models distance for clustering utility functio...
Outlier detection is an important problem occurring in a wide range of areas. Outliers are the outco...
The problem of curve clustering when curves are misaligned is considered. A novel algorithm is descr...
Mahalanobis-type distances in which the shape matrix is derived from a consistent highbreakdown robu...
Abstract: In modern era there are lots of data mining algorithms which focus on clustering methods. ...
In this paper we examine some of the relationships between two important optimization problems that ...