Name
Title | ||
|---|---|---|
Building a Decision Cluster Classification Model for High Dimensional Data by a Variable Weighting k-Means Method |
Abstract | ||
|---|---|---|
In this paper, a new classification method (ADCC) for high dimensional data is proposed. In this method, a decision cluster classification model (DCC) consists of a set of disjoint decision clusters, each labeled with a dominant class that determines the class of new objects falling in the cluster. A cluster tree is first generated from a training data set by recursively calling a variable weighting k -means algorithm. Then, the DCC model is selected from the tree. Anderson-Darling test is used to determine the stopping condition of the tree growing. A series of experiments on both synthetic and real data sets have shown that the new classification method (ADCC) performed better in accuracy and scalability than the existing methods of k -NN , decision tree and SVM. It is particularly suitable for large, high dimensional data with many classes. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-540-89378-3_33 | Australasian Conference on Artificial Intelligence |
Keywords | Field | DocType |
disjoint decision cluster,training data,decision cluster classification model,existing method,new classification method,decision tree,high dimensional data,new object,cluster tree,variable weighting k-means method,k means algorithm,classification,k means,clustering | Decision tree,k-means clustering,Data set,Clustering high-dimensional data,Weighting,Pattern recognition,Support vector machine,Artificial intelligence,Cluster analysis,Decision tree learning,Mathematics | Conference |
Volume | ISSN | Citations |
5360 | 0302-9743 | 5 |
PageRank | References | Authors |
0.48 | 6 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yan Li | 1 | 101 | 11.46 |
| Edward Hung | 2 | 263 | 15.72 |
| Chung Korris | 3 | 11 | 1.28 |
| Joshua Huang | 4 | 16 | 2.14 |