Paper Info
Title
Fuzzy clustering and decision tree learning for time-series tidal data classification
Abstract
In this paper, a hybrid decision tree learning approach is presented that combines fuzzy C-means method and the ID3 algorithm in decision tree construction from continuous-valued features. The fuzzy C-means method is applied to find a number of central means for each continuous-valued feature and thus discretize such features. The ID3 algorithm is subsequently used to build a decision tree from the discretized data. Preliminary experiments using a real-world time-series data set from the Louisiana coast are reported that compare our method with the OC1 system for oblique decision tree learning. The experiment results seem to suggest that the proposed hybrid method achieves better or comparable classification accuracy.
Year
DOI
Venue
2003
10.1109/FUZZ.2003.1209454
FUZZ-IEEE
Keywords
Field
DocType
real-world time-series data,fuzzy clustering,hybrid learning approach,fuzzy set theory,geophysics computing,storms,harmonic tidal data,pattern clustering,feature-value vectors,decision tree learning,learning (artificial intelligence),tides,time-series tidal data classification,continuous-valued features,artificial tidal record,id3 algorithm,louisiana coast,data mining,discrete attributes,decision trees,time series,hurricane,fuzzy c-means method,hurricanes,computer science,learning artificial intelligence,tropical cyclones,time series data,decision tree,clustering algorithms,system testing
Information Fuzzy Networks,Data mining,Decision tree,Computer science,Logistic model tree,Decision tree model,Artificial intelligence,ID3 algorithm,Machine learning,Decision tree learning,Alternating decision tree,Incremental decision tree
Conference
Volume
ISBN
Citations 
1
0-7803-7810-5
0
PageRank 
References 
Authors
0.34
3
3
Name
Order
Citations
PageRank
Jiwen Chen100.34
Jianhua Chen200.34
George P. Kemp300.34