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k-means clustering

235 papers

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Also known as

K-MEANS|| K-MEANS++ KM K-MEANS

Co-occurring keywords

approximation algorithm (630) clustering algorithm (206) unsupervised learning (3255) k-median clustering (31) differential privacy (1010) coreset construction (40) outlier detection (318) spectral clustering (304) dimensionality reduction (720) multi-view clustering (199)

Papers

Differentially Private Clustering: Tight Approximation Ratios NIPS 2020
Extending an Acoustic Data-Driven Phone Set for Spontaneous Speech Recognition INTERSPEECH 2019
Kernel Normalized Cut: a Theoretical Revisit ICML 2019
Balanced Clustering: A Uniform Model and Fast Algorithm IJCAI 2019
Coresets for Clustering with Fairness Constraints NIPS 2019
Proportionally Fair Clustering ICML 2019
Vector of Locally-Aggregated Word Embeddings (VLAWE): A Novel Document-level Representation NAACL 2019
Greedy Sampling for Approximate Clustering in the Presence of Outliers NIPS 2019
Power k-Means Clustering ICML 2019
k-Means Clustering of Lines for Big Data NIPS 2019
A Machine Learning Based Clustering Protocol for Determining Hearing Aid Initial Configurations from Pure-Tone Audiograms INTERSPEECH 2019
On Euclidean k-Means Clustering with alpha-Center Proximity AISTATS 2019
Multi-view Clustering via Late Fusion Alignment Maximization IJCAI 2019
Making AI Forget You: Data Deletion in Machine Learning NIPS 2019
Determinantal Point Processes for Coresets JMLR 2019
Identifying Temporal Trends Based on Perplexity and Clustering: Are We Looking at Language Change? ACL 2019
A Better k-means++ Algorithm via Local Search ICML 2019
Fair Algorithms for Clustering NIPS 2019
Unsupervised Detection of Metaphorical Adjective-Noun Pairs NAACL 2018
Sentence Suggestion of Japanese Functional Expressions for Chinese-speaking Learners ACL 2018
Data-Driven Clustering via Parameterized Lloyd's Families NIPS 2018
Query K-means Clustering and the Double Dixie Cup Problem NIPS 2018
A Practical Algorithm for Distributed Clustering and Outlier Detection NIPS 2018
K-means clustering using random matrix sparsification ICML 2018
Deep k-Means: Re-Training and Parameter Sharing with Harder Cluster Assignments for Compressing Deep Convolutions ICML 2018