Abstract
In this paper a novel data mining algorithm, Clustering and Classification Algorithm-Supervised (CCA-S), is introduced. CCA-S enables the scalable, incremental learning of a non-hierarchical cluster structure from training data. This cluster structure serves as a function to map the attribute values of new data to the target class of these data, that is, classify new data. CCA-S utilizes both the distance and the target class of training data points to derive the cluster structure. In this paper, we first present problems with many existing data mining algorithms for classification problems, such as decision trees, artificial neural networks, in scalable and incremental learning. We then describe CCA-S and discuss its advantages in scalable, incremental learning. The testing results of applying CCA-S to several common data sets for classification problems are presented. The testing results show that the classification performance of CCA-S is comparable to the other data mining algorithms such as decision trees, artificial neural networks and discriminant analysis.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 677-692 |
| Number of pages | 16 |
| Journal | Computers and Industrial Engineering |
| Volume | 43 |
| Issue number | 4 |
| DOIs | |
| State | Published - Sep 2002 |
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Keywords
- Classification
- Data mining
- Incremental learning
- Scalability
ASJC Scopus subject areas
- Management Science and Operations Research
- Information Systems and Management
- Industrial and Manufacturing Engineering
- Applied Mathematics
Cite this
A scalable, incremental learning algorithm for classification problems. / Ye, Nong; Li, Xiangyang.
In: Computers and Industrial Engineering, Vol. 43, No. 4, 09.2002, p. 677-692.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - A scalable, incremental learning algorithm for classification problems
AU - Ye, Nong
AU - Li, Xiangyang
PY - 2002/9
Y1 - 2002/9
N2 - In this paper a novel data mining algorithm, Clustering and Classification Algorithm-Supervised (CCA-S), is introduced. CCA-S enables the scalable, incremental learning of a non-hierarchical cluster structure from training data. This cluster structure serves as a function to map the attribute values of new data to the target class of these data, that is, classify new data. CCA-S utilizes both the distance and the target class of training data points to derive the cluster structure. In this paper, we first present problems with many existing data mining algorithms for classification problems, such as decision trees, artificial neural networks, in scalable and incremental learning. We then describe CCA-S and discuss its advantages in scalable, incremental learning. The testing results of applying CCA-S to several common data sets for classification problems are presented. The testing results show that the classification performance of CCA-S is comparable to the other data mining algorithms such as decision trees, artificial neural networks and discriminant analysis.
AB - In this paper a novel data mining algorithm, Clustering and Classification Algorithm-Supervised (CCA-S), is introduced. CCA-S enables the scalable, incremental learning of a non-hierarchical cluster structure from training data. This cluster structure serves as a function to map the attribute values of new data to the target class of these data, that is, classify new data. CCA-S utilizes both the distance and the target class of training data points to derive the cluster structure. In this paper, we first present problems with many existing data mining algorithms for classification problems, such as decision trees, artificial neural networks, in scalable and incremental learning. We then describe CCA-S and discuss its advantages in scalable, incremental learning. The testing results of applying CCA-S to several common data sets for classification problems are presented. The testing results show that the classification performance of CCA-S is comparable to the other data mining algorithms such as decision trees, artificial neural networks and discriminant analysis.
KW - Classification
KW - Data mining
KW - Incremental learning
KW - Scalability
UR - http://www.scopus.com/inward/record.url?scp=0036712901&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0036712901&partnerID=8YFLogxK
U2 - 10.1016/S0360-8352(02)00132-8
DO - 10.1016/S0360-8352(02)00132-8
M3 - Article
AN - SCOPUS:0036712901
VL - 43
SP - 677
EP - 692
JO - Computers and Industrial Engineering
JF - Computers and Industrial Engineering
SN - 0360-8352
IS - 4
ER -