Feature Weighting in k-Means Clustering
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Summary
An abstract framework for integrating multiple feature spaces in the k-means clustering algorithm is presented and the effectiveness of feature weighting in clustering on several different application domains is demonstrated.
- Type
- article
- Published
- 2003-09-01
- Cited by
- 400
- References
- 37
- Access
- Open access
- OpenAlex
- https://openalex.org/W1959178355
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:2733523
Keywords
Weighting, Feature (linguistics), Pattern recognition (psychology), Cluster analysis, Feature vector
References
- User-Chosen Phrases in Interactive Query Formulation for Information Retrieval
- An Analysis of Statistical and Syntactic Phrases
- A Microeconomic View of Data Mining
- Selection of Relevant Features and Examples in Machine Learning
- Model Selection in Unsupervised Learning with Applications To Document Clustering
- Refining Initial Points for K-Means Clustering
- Concept Decompositions for Large Sparse Text Data Using Clustering
- A Review and Empirical Evaluation of Feature Weighting Methods for a Class of Lazy Learning Algorithms
- Toward Optimal Feature Selection
- Feature Selection as a Preprocessing Step for Hierarchical Clustering
- Fuzzy clustering model for fuzzy data
- Introduction to Modern Information Retrieval
- Convexity and the Hemisphere
- Term-Weighting Approaches in Automatic Text Retrieval
- Clustering hypertext with applications to web searching
- A theory of term importance in automatic text analysis
- Numerical Recipes in C: The Art of Scientific Computing
- Knowledge Acquisition Via Incremental Conceptual Clustering
- Pattern classification and scene analysis
- UCI Repository of machine learning databases
Cited by
- OKM : une extension des k-moyennes pour la recherche de classes recouvrantes
- A Hierarchical Clustering and Validity Index for Mixed Data
- A survey on soft subspace clustering
- Geodesic distances for clustering linked text data
- A review of feature selection methods with applications
- Fuzzy Q-Learning with the modified fuzzy ART neural network
- Feature weighing for efficient clustering
- Semi-supervised Variable Weighting for Clustering
- Speech Recognition Using Vector Quantization through Modified K-meansLBG Algorithm
- State space segmentation for acquisition of agent behavior
- A heuristic algorithm for clustering rooted ordered trees
- A Local SVD Framework for Stable Feature Selection for Clustering
- Bagging-Based Selective Clusterer Ensemble
- Date clustering using Principal Component Analysis and Particle Swarm Optimization
- Weighting variables in K-means clustering
- Scalable Clustering Algorithms with Balancing Constraints
- Efficient fetal size classification combined with artificial neural network for estimation of fetal weight.
- An improved differential evolution and its application to determining feature weights in similarity based clustering
- Kernel methods for point symmetry-based clustering
- Part Family Formation Based on Clustering Algorithm and BP Network
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