Feature Selection for Clustering

By Manoranjan Dash, Poon Wei Koot. Encyclopedia of Database Systems, 2000

331 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems

View Paper: https://www.semanticscholar.org/paper/8c94656d51f49a65bd53e8ad1902c12920a7e5fb

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In Artificial Intelligence

In Computer Vision and Pattern Recognition

In Information Systems

Citations in the corpus

Subspace clustering for high dimensional data: a review (2004, methodology): “Another hybrid approach uses a greedy algorithm to rank features based on entropy values and then uses k -means to select the best subsets of features [20].”
An Evaluation on Feature Selection for Text Clustering (2003, methodology): “Entropy-based ranking is proposed by Dash and Liu (2000).”
Toward integrating feature selection algorithms for classification and clustering (2005, background): “developing dependent criteria in feature selection for clustering can been found in [20], [27], [42].”
A review of unsupervised feature selection methods (2019, background): “, 2017) (Guo and Zhu, 2018) (Dash and Liu, 2000) (Hruschka et al.”
Supervised, Unsupervised, and Semi-Supervised Feature Selection: A Review on Gene Selection (2016, background): “Hence, this paper will only discuss feature selection since it is considered to be more superior in terms of readability and interpretability [5].”
Mind the Gap: A Generative Approach to Interpretable Feature Selection and Extraction (2015, background): “[13, 14, 15]) first select important dimensions and then cluster based on those.”
Improving fuzzy c-means clustering based on feature-weight learning (2004, methodology): “…literatures one can find a number of commonly used feature selection algorithms including of sequential unsupervised feature selection algorithms (Dash and Liu, 2000), wrapper approaches based on expectation maximization (Dy and Brodely, 2000), maximum entropy based methods (Basu et al., 2000),…”
Feature selection for clustering - a filter solution (2002, methodology): “Examples of global methods are [8, 9, 11, 17, 20, 21] and the proposed method in this paper.”
A Survey of Text Clustering Algorithms (2012, methodology): “The entropy-based ranking approach was proposed in [27].”
Simultaneous feature selection and clustering using mixture models (2004, methodology): “In [13], the clustering tendency of each feature is assessed by an entropy index.”
Feature Selection: A literature Review (2014, neutral): “Filter + Wrapper BBHFS [82], Xing’s [91] Dash-Liu’s [81]”