Name
Title | ||
|---|---|---|
Categorical Data Clustering: A Correlation-Based Approach for Unsupervised Attribute Weighting |
Abstract | ||
|---|---|---|
The interest in attribute weighting, in clustering tasks, have been increasing in the last years. However, few attempts have been made to apply automated attribute weighting to categorical data clustering. Most of the existing approaches computes the weights based on the frequency of the mode category or according to the average distance of data objects from the mode of a cluster. In this paper, we adopt a different approach, investigating how to use the correlation among categorical attributes for measuring their relevancies in clustering tasks. As a result, we propose a correlation-based attribute weighting approach for categorical attributes. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICTAI.2014.46 | Tools with Artificial Intelligence |
Keywords | Field | DocType |
data mining,pattern clustering,automated attribute weighting,average data object distance,categorical attributes,categorical data clustering task,correlation-based attribute weighting approach,mode category frequency,unsupervised attribute weighting,attribute weighting,categorical data,clustering,data mining,subspace clustering | Fuzzy clustering,Data mining,CURE data clustering algorithm,Computer science,Categorical variable,Artificial intelligence,Conceptual clustering,Cluster analysis,Single-linkage clustering,Clustering high-dimensional data,Pattern recognition,Correlation clustering,Machine learning | Conference |
ISSN | Citations | PageRank |
1082-3409 | 4 | 0.44 |
References | Authors | |
10 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Joel Luis Carbonera | 1 | 4 | 0.44 |
| Mara Abel | 2 | 6 | 1.54 |