Data clustering is essential problem in database technology – successful solutions in this field provide data storing and accessing optimizations, which yield better performance characteristics. Another advantage of clustering is in relation with ability to distinguish similar data patterns and semantically interconnected entities. This in turn is very valuable for data mining and knowledge discovery activities. Although many general clustering strategies and algorithms were developed in past years, this search is still far from end, as there are many potential implementation fields, each stating its own unique requirements. This paper describes data clustering based on original spatial partitioning of force-based graph layout, which provid...
Relational data clustering is the task of grouping data objects together when both features and rela...
Regionalisation, a prominent problem from social geography, could be solved by a classification algo...
A graph model is often used to represent complex relational information in data clustering. Although...
Most current data clustering algorithms in data mining are based on a distance calculation in certai...
In this paper, a new graphical approach to vertical partitioning of a relation is presented. The pro...
Ankara : The Department of Computer Engineering and the Graduate School of Engineering and Science o...
In this paper we propose a novel spatial clustering method, named CORSO, that resorts to a relationa...
Cluster graphs are a valuable concept to visualize structu-red relational information. Hierarchical ...
We consider the problem of clustering elements that have both content and relational information (e....
We consider the problem of clustering el-ements that have both content and rela-tional information (...
Relational data clustering is the task of grouping data ob-jects together when both features and rel...
Clustering methods are particularly well-suited for identifying classes in spatial databases. Howeve...
Abstract—With the wide application of RDF(Resource Description Framework) data, the data volume grow...
AbstractK-means is one of the most popular clustering algorithms. This article introduces an efcient...
Relational data clustering is the task of grouping data objects together when both attributes and re...
Relational data clustering is the task of grouping data objects together when both features and rela...
Regionalisation, a prominent problem from social geography, could be solved by a classification algo...
A graph model is often used to represent complex relational information in data clustering. Although...
Most current data clustering algorithms in data mining are based on a distance calculation in certai...
In this paper, a new graphical approach to vertical partitioning of a relation is presented. The pro...
Ankara : The Department of Computer Engineering and the Graduate School of Engineering and Science o...
In this paper we propose a novel spatial clustering method, named CORSO, that resorts to a relationa...
Cluster graphs are a valuable concept to visualize structu-red relational information. Hierarchical ...
We consider the problem of clustering elements that have both content and relational information (e....
We consider the problem of clustering el-ements that have both content and rela-tional information (...
Relational data clustering is the task of grouping data ob-jects together when both features and rel...
Clustering methods are particularly well-suited for identifying classes in spatial databases. Howeve...
Abstract—With the wide application of RDF(Resource Description Framework) data, the data volume grow...
AbstractK-means is one of the most popular clustering algorithms. This article introduces an efcient...
Relational data clustering is the task of grouping data objects together when both attributes and re...
Relational data clustering is the task of grouping data objects together when both features and rela...
Regionalisation, a prominent problem from social geography, could be solved by a classification algo...
A graph model is often used to represent complex relational information in data clustering. Although...