Centroid-Based Document Classification: Analysis and Experimental Results

By Eui-Hong Sam Han, George Karypis. European Conference on Principles of Data Mining and Knowledge Discovery, 2000

486 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/8067a47e1a62b18abf6b453e280a6fbff6a3ce1e

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Citations in the corpus

A Survey of Text Classification Algorithms (2012, methodology): “The work in [55] shows that there are some advantages in designing schemes in which the similarity computations take account of the dependencies between the terms of the different classes.”
Quantifying counts and costs via classification (2008, methodology): “The benchmark text classification tasks are drawn from OHSUMED abstracts (ohscal), Los Angeles Times articles of 1989 and 1990 (la), and the Foreign Broadcast Information Service (fbis) (Han and Karypis 2000).”
Just-in-time contextual advertising (2007, methodology): “Suitable candidates include the nearest neighbor and the Naive Bayes classifier [9], as well as prototype formation methods such as Rocchio [21] or centroidbased [12] classifiers.”
CANN: An intrusion detection system based on combining cluster centers and nearest neighbors (2015, background): “The idea behind CANN is that the cluster centers or centroids for a given dataset offer discrimination capabilities for recognition both similar and dissimilar classes [9,10,35].”
An Extensive Empirical Study of Feature Selection Metrics for Text Classification (2003, methodology): “Datasets: We were fortunate to obtain a large number of text classification problems in preprocessed form made available by Han and Karypis (2000), the details of which are laid out in their paper and in 0.”
Deep feature weighting for naive Bayes and its application to text classification (2016, background): “Text classification presents unique challenges due to large number of features, large number of documents and strong dependencies among features (Han and Karypis, 2000; Han et al., 2001).”
An empirical study of sentiment analysis for chinese documents (2008, unknown): “…gain (IG), CHI statistics (CHI), document frequency (DF), and five learning methods, i.e., K-nearest neighbor (KNN) (Yang & Lin, 1999), centroid classifier (Han & Karypis, 2000), Naı¨ve Bayes (NB) (McCallum & Kamal, 1998), winnow (Zhang, 2001) and support vector machine (SVM) (Joachims, 1998).”
Computing Semantic Relatedness Using Wikipedia-based Explicit Semantic Analysis (2007, methodology): “We implemented the semantic interpreter as a centroidbased classifier [Han and Karypis, 2000], which, given a text fragment, ranks all the Wikipedia concepts by their relevance to the fragment.”
Text Classification Algorithms: A Survey (2019, methodology): “In general, multi-class SVM is an optimization problem of the following form: min w1,w2,..,wk ,ζ 1 2 ∑k wTk wk + C ∑ (xi ,yi)∈D ζi (111) st. wTyi x− w T k x ≤ i− ζi, ∀(xi, yi) ∈ D, k ∈ {1, 2, ..., K}, k 6= yi (112) where (xi, yi) represent the training data points such that (xi, yi) ∈ D, C is the pe”
Neighbor-weighted K-nearest neighbor for unbalanced text corpus (2005, methodology): “A number of machine learning algorithms have been introduced to deal with text classification, such as K-Nearest Neighbor (KNN) (Yang, 1999; Yang & Lin, 1999), Centroid-Based Classifier ( Han & Karypis, 2000 ), Naive Bayes (Lewis, 1998), Decision Trees (Lewis & Ringuette, 1994) and Support Vector Ma”
Overcoming the Brittleness Bottleneck using Wikipedia: Enhancing Text Categorization with Encyclopedic Knowledge (2006, methodology): “Conventional induction techniques can hardly be applied in these settings, so we opted to use a simple and efficient centroid classifier ( Han & Karypis 2000 ), which represents each concept with an attribute1 vector of the article text.”