Cluster analysis is a popular unsupervised learning method. Its goal is to find a partition of a dataset of N objects into k well separated groups: elements within a group must be similar (in some sense) to one another, and different to elements in other groups. The fundamental problem of cluster analysis is to determine the real number of groups (k) in the dataset. In this paper, a new method of clustering is presented, to simultaneously determine the number of groups and the clustering in a dataset. This method is based on graph theory. Dissimilarity data between objects is used to form a dissimilarity graph, in which the vertices are the objects in dataset, and the edges are weighted according to the dissimilarity between the objects. Tw...
Clustering is the process of grouping a set ofphysical or abstract objects into classes of similarob...
Cluster study or clustering is the assignment of assigning a set of data into groups called clusters...
An integrated framework for density-based cluster analysis, outlier detection, and data visualizatio...
We have developed a novel algorithm for cluster analysis that is based on graph theoretic techniques...
Cluster and discriminant analysis belong to basic classification methods. Using cluster analysis can...
This chapter deals with basic tools useful in clustering and classification and present some commonl...
Cluster analysis is an important problem in data mining and machine learning. In reality, clustering...
Abstract: An Outlier is an extreme value in a data set. Using clustering techniques we can detect ou...
Clustering analysis is one of the main tools for exploratory data analysis, with applications from s...
Abstract: In modern era there are lots of data mining algorithms which focus on clustering methods. ...
Cluster analysis characterizes data that are similar enough and useful into meaningful groups (clust...
[[abstract]]In this paper, a two-phase clustering algorithm for outliers detection is proposed. We f...
Cluster analysis is a popular method of multivariate statistics. Based on mutual similarities betwee...
Partitioning data into a finite number of k homogenous and separate clusters (groups) without use of...
In this paper, we present our research on data mining approaches with the existence of obstacles. Al...
Clustering is the process of grouping a set ofphysical or abstract objects into classes of similarob...
Cluster study or clustering is the assignment of assigning a set of data into groups called clusters...
An integrated framework for density-based cluster analysis, outlier detection, and data visualizatio...
We have developed a novel algorithm for cluster analysis that is based on graph theoretic techniques...
Cluster and discriminant analysis belong to basic classification methods. Using cluster analysis can...
This chapter deals with basic tools useful in clustering and classification and present some commonl...
Cluster analysis is an important problem in data mining and machine learning. In reality, clustering...
Abstract: An Outlier is an extreme value in a data set. Using clustering techniques we can detect ou...
Clustering analysis is one of the main tools for exploratory data analysis, with applications from s...
Abstract: In modern era there are lots of data mining algorithms which focus on clustering methods. ...
Cluster analysis characterizes data that are similar enough and useful into meaningful groups (clust...
[[abstract]]In this paper, a two-phase clustering algorithm for outliers detection is proposed. We f...
Cluster analysis is a popular method of multivariate statistics. Based on mutual similarities betwee...
Partitioning data into a finite number of k homogenous and separate clusters (groups) without use of...
In this paper, we present our research on data mining approaches with the existence of obstacles. Al...
Clustering is the process of grouping a set ofphysical or abstract objects into classes of similarob...
Cluster study or clustering is the assignment of assigning a set of data into groups called clusters...
An integrated framework for density-based cluster analysis, outlier detection, and data visualizatio...