C4.5: programs for machine learning
C4.5: programs for machine learning
A database perspective on knowledge discovery
Communications of the ACM
Building Decision Trees with Constraints
Data Mining and Knowledge Discovery
Learning Decision Trees Using the Area Under the ROC Curve
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Top-Down Induction of Clustering Trees
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
A perspective on inductive databases
ACM SIGKDD Explorations Newsletter
Theoretical Comparison between the Gini Index and Information Gain Criteria
Annals of Mathematics and Artificial Intelligence
Clustering short time series gene expression data
Bioinformatics
Looking for monotonicity properties of a similarity constraint on sequences
Proceedings of the 2006 ACM symposium on Applied computing
Introduction to Information Retrieval
Introduction to Information Retrieval
Clustering of time series data-a survey
Pattern Recognition
Value, cost, and sharing: open issues in constrained clustering
KDID'06 Proceedings of the 5th international conference on Knowledge discovery in inductive databases
Integrating decision tree learning into inductive databases
KDID'06 Proceedings of the 5th international conference on Knowledge discovery in inductive databases
An efficient algorithm for mining string databases under constraints
KDID'04 Proceedings of the Third international conference on Knowledge Discovery in Inductive Databases
Constraint based induction of multi-objective regression trees
KDID'05 Proceedings of the 4th international conference on Knowledge Discovery in Inductive Databases
Learning predictive clustering rules
KDID'05 Proceedings of the 4th international conference on Knowledge Discovery in Inductive Databases
Decision trees for hierarchical multi-label classification
Machine Learning
Which Distance for the Identification and the Differentiation of Cell-Cycle Expressed Genes?
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
Beam search induction and similarity constraints for predictive clustering trees
KDID'06 Proceedings of the 5th international conference on Knowledge discovery in inductive databases
ImageCLEF 2009 medical image annotation task: PCTs for hierarchical multi-label classification
CLEF'09 Proceedings of the 10th international conference on Cross-language evaluation forum: multimedia experiments
Network regression with predictive clustering trees
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
Global and local spatial autocorrelation in predictive clustering trees
DS'11 Proceedings of the 14th international conference on Discovery science
Decision forest: an algorithm for classifying multivariate time series
International Journal of Business Intelligence and Data Mining
Tree ensembles for predicting structured outputs
Pattern Recognition
A time-dependent enhanced support vector machine for time series regression
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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Predictive clustering is a general framework that unifies clustering and prediction. This paper investigates how to apply this framework to cluster time series data. The resulting system, Clus-TS, constructs predictive clustering trees (PCTs) that partition a given set of time series into homogeneous clusters. In addition, PCTs provide a symbolic description of the clusters. We evaluate Clus-TS on time series data from microarray experiments. Each data set records the change over time in the expression level of yeast genes as a response to a change in environmental conditions. Our evaluation shows that Clus-TS is able to cluster genes with similar responses, and to predict the time series based on the description of a gene. Clus-TS is part of a larger project where the goal is to investigate how global models can be combined with inductive databases.