C4.5: programs for machine learning
C4.5: programs for machine learning
Efficient mining of association rules using closed itemset lattices
Information Systems
Generating non-redundant association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
Data Mining and Knowledge Discovery
Discretization: An Enabling Technique
Data Mining and Knowledge Discovery
Formalizing Hypotheses with Concepts
ICCS '00 Proceedings of the Linguistic on Conceptual Structures: Logical Linguistic, and Computational Issues
Quantization of Continuous Input Variables for Binary Classification
IDEAL '00 Proceedings of the Second International Conference on Intelligent Data Engineering and Automated Learning, Data Mining, Financial Engineering, and Intelligent Agents
Using Closed Itemsets for Discovering Representative Association Rules
ISMIS '00 Proceedings of the 12th International Symposium on Foundations of Intelligent Systems
An Introduction to Database Systems
An Introduction to Database Systems
LCM ver.3: collaboration of array, bitmap and prefix tree for frequent itemset mining
Proceedings of the 1st international workshop on open source data mining: frequent pattern mining implementations
Label propagation through linear neighborhoods
ICML '06 Proceedings of the 23rd international conference on Machine learning
TRIAS--An Algorithm for Mining Iceberg Tri-Lattices
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Mathematical Tools for Data Mining: Set Theory, Partial Orders, Combinatorics
Mathematical Tools for Data Mining: Set Theory, Partial Orders, Combinatorics
Label ranking by learning pairwise preferences
Artificial Intelligence
Database Systems: The Complete Book
Database Systems: The Complete Book
A New Search Results Clustering Algorithm Based on Formal Concept Analysis
FSKD '08 Proceedings of the 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 02
Learning Markov logic network structure via hypergraph lifting
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
RESTRUCTURING LATTICE THEORY: AN APPROACH BASED ON HIERARCHIES OF CONCEPTS
ICFCA '09 Proceedings of the 7th International Conference on Formal Concept Analysis
Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
Mining multidimensional and multilevel sequential patterns
ACM Transactions on Knowledge Discovery from Data (TKDD)
Introduction to Semi-Supervised Learning
Introduction to Semi-Supervised Learning
Topological properties of concept spaces (full version)
Information and Computation
From digital genetics to knowledge discovery: Perspectives in genetic network understanding
Intelligent Data Analysis - Knowledge Discovery in Bioinformatics
Semi-Supervised Learning
Predicting partial orders: ranking with abstention
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
Preference Learning
Mining gene expression data with pattern structures in formal concept analysis
Information Sciences: an International Journal
Learning closed sets of labeled graphs for chemical applications
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
Revisiting numerical pattern mining with formal concept analysis
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
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We propose a new approach for semi-supervised learning using closed set lattices, which have been recently used for frequent pattern mining within the framework of the data analysis technique of Formal Concept Analysis FCA. We present a learning algorithm, called SELF SEmi-supervised Learning via FCA, which performs as a multiclass classifier and a label ranker for mixed-type data containing both discrete and continuous variables, while only few learning algorithms such as the decision tree-based classifier can directly handle mixed-type data. From both labeled and unlabeled data, SELF constructs a closed set lattice, which is a partially ordered set of data clusters with respect to subset inclusion, via FCA together with discretizing continuous variables, followed by learning classification rules through finding maximal clusters on the lattice. Moreover, it can weight each classification rule using the lattice, which gives a partial order of preference over class labels. We illustrate experimentally the competitive performance of SELF in classification and ranking compared to other learning algorithms using UCI datasets.