AbstractA new base on grid clustering method is presented in this paper. This new method first does unsupervised learning on the high dimensions data. This paper proposed a grid-based approach to clustering. It maps the data onto a multi-dimensional space and applies a linear transformation to the feature space instead of to the objects themselves and then approach a grid-clustering method. Unlike the conventional methods, it uses a multidimensional hyper-eclipse grid cell. Some case studies and ideas how to use the algorithms are described. The experimental results show that EGC can discover abnormity shapes of clusters
[[abstract]]The grid-based clustering algorithm is an efficient clustering algorithm, but its effect...
The objective of data mining is to take out information from large amounts of data and convert it in...
An algorithm for optimizing data clustering in feature space is studied in this work. Using graph La...
AbstractA new base on grid clustering method is presented in this paper. This new method first does ...
Clustering is an essential way to extract meaningful information from massive data without human int...
[[abstract]]The grid-based clustering algorithm, which partitions the data space into a finite numbe...
[[abstract]]These spatial clustering methods can be classified into four categories: partitioning me...
Many applications require the clustering of large amounts of high-dimensional data. Most clustering ...
This paper presents a supervised clustering algorithm, namely Grid-Based Supervised Clustering (GBSC...
[[abstract]]The grid-based clustering algorithm is an efficient clustering algorithm, but the effect...
Projection clustering is an important cluster problem. Although there are extensive studies with pro...
We propose an enhanced grid-density based approach for clustering high dimensional data. Our techniq...
Abstract. A new clustering algorithm based on grid projections is proposed. This algorithm, called U...
Density-based and grid-based clustering are two main clustering approaches. The former is famous for...
Density-based and grid-based clustering are two main clustering approaches. The former is famous for...
[[abstract]]The grid-based clustering algorithm is an efficient clustering algorithm, but its effect...
The objective of data mining is to take out information from large amounts of data and convert it in...
An algorithm for optimizing data clustering in feature space is studied in this work. Using graph La...
AbstractA new base on grid clustering method is presented in this paper. This new method first does ...
Clustering is an essential way to extract meaningful information from massive data without human int...
[[abstract]]The grid-based clustering algorithm, which partitions the data space into a finite numbe...
[[abstract]]These spatial clustering methods can be classified into four categories: partitioning me...
Many applications require the clustering of large amounts of high-dimensional data. Most clustering ...
This paper presents a supervised clustering algorithm, namely Grid-Based Supervised Clustering (GBSC...
[[abstract]]The grid-based clustering algorithm is an efficient clustering algorithm, but the effect...
Projection clustering is an important cluster problem. Although there are extensive studies with pro...
We propose an enhanced grid-density based approach for clustering high dimensional data. Our techniq...
Abstract. A new clustering algorithm based on grid projections is proposed. This algorithm, called U...
Density-based and grid-based clustering are two main clustering approaches. The former is famous for...
Density-based and grid-based clustering are two main clustering approaches. The former is famous for...
[[abstract]]The grid-based clustering algorithm is an efficient clustering algorithm, but its effect...
The objective of data mining is to take out information from large amounts of data and convert it in...
An algorithm for optimizing data clustering in feature space is studied in this work. Using graph La...