Add to ORKGPopulation based real-life datasets often contain smaller clusters of unusual sub-populations. While these clusters, called 'hot spots', are small and sparse, they are usually of special interest to an analyst. In this paper we introduce a visual drill-down Self-Organizing Map (SOM)-based approach to explore such hot spots characteristics in real-life datasets. Iterative clustering algorithms (such as k-means) and SOM are not designed to show these small and sparse clusters in detail. The feasibility of our approach is demonstrated using a large real life dataset from the Australian Taxation Office
Abstract –A new clustering algorithm based on emergent SOM is proposed. This algorithm, called U*C, ...
Cluster analysis methods are used to classify R unlabeled objects in a P-dimensional space into grou...
The Self-Organizing Map (SOM) is one of the most popular neural network meth-ods. It is a powerful t...
Real-life datasets often contain small clusters of unusual sub-populations. These clusters, or 'hot ...
Discovering clustering changes in real-life datasets is important in many contexts, such as fraud de...
The cluster analysis has been widely applied to many fields. In this dissertation, Hot spot detectio...
We introduce a Self-Organizing Map (SOM) based visualization method that compares cluster structures...
We introduce a Self-Organizing Map (SOM)-based visualization method that compares cluster structures...
Abstract: This paper describes an approach to pixel clustering using self-organising map (SOM) techn...
Spatial data mining seeks to discover meaningful patterns in data where a prime dimension of interes...
Distribution of socio-economic features in urban space is an important source of information for lan...
Local Moran and local G-statistic are commonly used to identify high-value (hot spot) and low-value ...
Abstract—The self-organizing map (SOM) is an excellent tool in exploratory phase of data mining. It ...
In the article, an additional visualization of self-organizing maps (SOM) has been investigated. The...
Abstract. A known approach for the detection of hotspots is to use a cluster technique, which is an ...
Abstract –A new clustering algorithm based on emergent SOM is proposed. This algorithm, called U*C, ...
Cluster analysis methods are used to classify R unlabeled objects in a P-dimensional space into grou...
The Self-Organizing Map (SOM) is one of the most popular neural network meth-ods. It is a powerful t...
Real-life datasets often contain small clusters of unusual sub-populations. These clusters, or 'hot ...
Discovering clustering changes in real-life datasets is important in many contexts, such as fraud de...
The cluster analysis has been widely applied to many fields. In this dissertation, Hot spot detectio...
We introduce a Self-Organizing Map (SOM) based visualization method that compares cluster structures...
We introduce a Self-Organizing Map (SOM)-based visualization method that compares cluster structures...
Abstract: This paper describes an approach to pixel clustering using self-organising map (SOM) techn...
Spatial data mining seeks to discover meaningful patterns in data where a prime dimension of interes...
Distribution of socio-economic features in urban space is an important source of information for lan...
Local Moran and local G-statistic are commonly used to identify high-value (hot spot) and low-value ...
Abstract—The self-organizing map (SOM) is an excellent tool in exploratory phase of data mining. It ...
In the article, an additional visualization of self-organizing maps (SOM) has been investigated. The...
Abstract. A known approach for the detection of hotspots is to use a cluster technique, which is an ...
Abstract –A new clustering algorithm based on emergent SOM is proposed. This algorithm, called U*C, ...
Cluster analysis methods are used to classify R unlabeled objects in a P-dimensional space into grou...
The Self-Organizing Map (SOM) is one of the most popular neural network meth-ods. It is a powerful t...