Papers Vector Quantization (k-means problem)
“Vector Quantization (k-means problem)” 태그가 달린 논문 5편 · 필터 해제
Data Aggregation for Hierarchical Clustering
Hierarchical Agglomerative Clustering (HAC) is likely the earliest and most flexible clustering method, because it can be used with many distances, similarities, and various linkage strategies. It is often used when the …
ClusteringVector Quantization (k-means problem)The Effect of Points Dispersion on the $k$-nn Search in Random Projection Forests
Partitioning trees are efficient data structures for $k$-nearest neighbor search. Machine learning libraries commonly use a special type of partitioning trees called $k$d-trees to perform $k$-nn search. Unfortunately, $k…
Instance SearchQuantizationVector Quantization (k-means problem)Breathing K-Means: Superior K-Means Solutions through Dynamic K-Values
We introduce the breathing k-means algorithm, which on average significantly improves solutions obtained by the widely-known greedy k-means++ algorithm, the default method for k-means clustering in the scikit-learn packa…
Vector Quantization (k-means problem)Fast K-Means with Accurate Bounds
We propose a novel accelerated exact k-means algorithm, which performs better than the current state-of-the-art low-dimensional algorithm in 18 of 22 experiments, running up to 3 times faster. We also propose a general i…
ClusteringVector Quantization (k-means problem)Learning the k in k-means
When clustering a dataset, the right number k of clusters to use is often not obvious, and choosing k automatically is a hard algorithmic problem. In this paper we present an improved algorithm for learning k while clust…
ClusteringVector Quantization (k-means problem)