Trading MIPS and memory for knowledge engineering
Communications of the ACM
Automatic indexing based on Bayesian inference networks
SIGIR '93 Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval
An example-based mapping method for text categorization and retrieval
ACM Transactions on Information Systems (TOIS)
Towards language independent automated learning of text categorization models
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Ordered and Unordered Tree Inclusion
SIAM Journal on Computing
Training algorithms for linear text classifiers
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Context-sensitive learning methods for text categorization
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Algorithms on Trees and Graphs
Algorithms on Trees and Graphs
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Hierarchical Text Classification and Evaluation
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Indexing and Mining Free Trees
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Efficient Data Mining for Maximal Frequent Subtrees
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
A Linear Least Squares Fit mapping method for information retrieval from natural language texts
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
SSDBM '04 Proceedings of the 16th International Conference on Scientific and Statistical Database Management
An Evaluation of Approaches to Classification Rule Selection
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Mining Closed and Maximal Frequent Subtrees from Databases of Labeled Rooted Trees
IEEE Transactions on Knowledge and Data Engineering
Efficiently Mining Frequent Trees in a Forest: Algorithms and Applications
IEEE Transactions on Knowledge and Data Engineering
Efficiently Mining Frequent Embedded Unordered Trees
Fundamenta Informaticae - Advances in Mining Graphs, Trees and Sequences
Frequent Subtree Mining - An Overview
Fundamenta Informaticae - Advances in Mining Graphs, Trees and Sequences
MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
Sequential pattern mining for structure-based XML document classification
INEX'05 Proceedings of the 4th international conference on Initiative for the Evaluation of XML Retrieval
Transforming XML trees for efficient classification and clustering
INEX'05 Proceedings of the 4th international conference on Initiative for the Evaluation of XML Retrieval
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Recently, tree structures have become a popular way for storing and manipulating huge amount of data. Classification of these data can facilitate storage, retrieval, indexing, query answering and different processing operations. In this paper, we present C-Classifier and M-Classifier algorithms for rule based classification of tree structured data. These algorithms are based on extracting especial tree patterns from training dataset. These tree patterns, i.e. closed tree patterns and maximal tree patterns are capable of extracting characteristics of training trees completely and non-redundantly. Our experiments show that M-Classifier significantly reduces running time and complexity. As experimental results show, accuracies of M-Classifier and C-Classifier depend on whether or not we want to classify all of the data points (even uncovered data). In the case of complete classification, C-Classifier shows the best classification quality. On the other hand and in the case of partial classification, M-Classifier improves classification quality measures.