Add to ORKGData clustering is the task of detecting patterns in a set of data. Most algorithms take non-relational data as input and are sometimes unable to find significant patterns. Many data sets can include relational information, as well as independent object attributes. We believe that clustering with relational data will help find significant patterns where non-relational algorithms fail. This paper discusses two open problems in relational data clustering: clustering heterogeneous data, and relation selection or extraction. Potential methods for addressing the problems are presented. 1
A large class of clustering problems can be formulated as an optimizational prob-lem in which the be...
The task of clustering is at the same time challenging and very important in Artificial Intelligence...
Traditional clustering approaches usually analyze static datasets in which objects are kept unchange...
Two types of data are used in pattern recognition, object and relational data. Object data is the mo...
Relational data clustering is the task of grouping data objects together when both features and rela...
This dissertation focuses on the topic of relational data clustering, which is the task of organizin...
Relational data clustering is a form of relational learn-ing that clusters data using the relational...
Relational data clustering is the task of grouping data ob-jects together when both features and rel...
Clustering is a data mining task to group objects such that data inside each cluste model the contin...
This paper is concerned with the computational efficiency of clustering algorithms when the data set...
Relational data clustering is the task of grouping data objects together when both attributes and re...
Research on the problem of clustering tends to be fragmented across the pattern recognition, databas...
This paper is concerned with the computational efficiency of clustering algorithms when the data set...
A large class of clustering problems can be formulated as an optimizational problem in which the bes...
Nowadays, the representation of many real word problems needs to use some type of relational model. ...
A large class of clustering problems can be formulated as an optimizational prob-lem in which the be...
The task of clustering is at the same time challenging and very important in Artificial Intelligence...
Traditional clustering approaches usually analyze static datasets in which objects are kept unchange...
Two types of data are used in pattern recognition, object and relational data. Object data is the mo...
Relational data clustering is the task of grouping data objects together when both features and rela...
This dissertation focuses on the topic of relational data clustering, which is the task of organizin...
Relational data clustering is a form of relational learn-ing that clusters data using the relational...
Relational data clustering is the task of grouping data ob-jects together when both features and rel...
Clustering is a data mining task to group objects such that data inside each cluste model the contin...
This paper is concerned with the computational efficiency of clustering algorithms when the data set...
Relational data clustering is the task of grouping data objects together when both attributes and re...
Research on the problem of clustering tends to be fragmented across the pattern recognition, databas...
This paper is concerned with the computational efficiency of clustering algorithms when the data set...
A large class of clustering problems can be formulated as an optimizational problem in which the bes...
Nowadays, the representation of many real word problems needs to use some type of relational model. ...
A large class of clustering problems can be formulated as an optimizational prob-lem in which the be...
The task of clustering is at the same time challenging and very important in Artificial Intelligence...
Traditional clustering approaches usually analyze static datasets in which objects are kept unchange...