Information-theoretic algorithm for feature selection
Pattern Recognition Letters
Unsupervised Feature Selection Using Feature Similarity
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Feature Weighting in k-Means Clustering
Machine Learning
Dimensionality Reduction of Unsupervised Data
ICTAI '97 Proceedings of the 9th International Conference on Tools with Artificial Intelligence
An introduction to variable and feature selection
The Journal of Machine Learning Research
Feature Selection for Unsupervised Learning
The Journal of Machine Learning Research
Improving fuzzy c-means clustering based on feature-weight learning
Pattern Recognition Letters
Efficient Feature Selection via Analysis of Relevance and Redundancy
The Journal of Machine Learning Research
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
Automated Variable Weighting in k-Means Type Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Attribute Clustering for Grouping, Selection, and Classification of Gene Expression Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A clustering-based method for unsupervised intrusion detections
Pattern Recognition Letters
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Mixed feature selection based on granulation and approximation
Knowledge-Based Systems
A new feature selection method for Gaussian mixture clustering
Pattern Recognition
A Cluster-Based Feature Selection Approach
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
Approximate Equal Frequency Discretization Method
GCIS '09 Proceedings of the 2009 WRI Global Congress on Intelligent Systems - Volume 03
Selecting discrete and continuous features based on neighborhood decision error minimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The feature selection problem: traditional methods and a new algorithm
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
| Hi-index | 0.00 |
Feature selection plays an important part in improving the quality of learning algorithms in machine learning and data mining. It has been widely studied in supervised learning, whereas it is still relatively rare researched in unsupervised learning. In this work, a clustering-based framework formed by an unsupervised feature selection algorithm is proposed. The proposed framework is mainly concerned with the problem of determining and choosing important features, which are selected by ranking the features according to the importance measure scores, from the original feature set without class information. Theory analyzed indicates that the time complexity of each algorithm is nearly linear with the size and the number of features of dataset. Experimental results on UCI datasets show that algorithm with different scores in the framework are able to identify the important features with clustering, and the proposed algorithm have obtained competitive results in terms of classification error rate and the degree of dimensionality reduction when compared with the state-of-the-art supervised and unsupervised feature selection approaches.