TY - GEN
T1 - Building a decision cluster classification model for high dimensional data by a variable weighting k-means method
AU - Li, Yan
AU - Hung, Edward
AU - Chung, Fu Lai Korris
AU - Huang, Joshua
PY - 2008/12/1
Y1 - 2008/12/1
N2 - 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.
AB - 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.
KW - Classification
KW - Clustering
KW - K-NN
KW - W-k-means
UR - http://www.scopus.com/inward/record.url?scp=58349085623&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-89378-3_33
DO - 10.1007/978-3-540-89378-3_33
M3 - Conference article published in proceeding or book
SN - 3540893776
SN - 9783540893776
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 337
EP - 347
BT - AI 2008
T2 - 21st Australasian Joint Conference on Artificial Intelligence, AI 2008
Y2 - 1 December 2008 through 5 December 2008
ER -