Merging $K$-means with hierarchical clustering for identifying general-shaped groups
Clustering partitions a dataset such that observations placed together in a group are similar but different from those in other groups. Hierarchical and $K$-means clustering are two approaches but have different strengths and weaknesses. For instance, hierarchical clustering identifies groups in a tree-like structure but suffers from computational complexity in large datasets while $K$-means clustering is efficient but designed to identify homogeneous spherically-shaped clusters. We present a hybrid non-parametric clustering approach that amalgamates the two methods to identify general-shaped clusters and that can be applied to larger datasets. Specifically, we first partition the dataset into spherical groups using $K$-means. We next merge these groups using hierarchical methods with a data-driven distance measure as a stopping criterion. Our proposal has the potential to reveal groups with general shapes and structure in a dataset. We demonstrate good performance on several simulated and real datasets.
Code (0)
등록된 구현이 없습니다.
Tasks
ClusteringDensity EstimationSpeech SynthesisSimilar Papers 제목 키워드 기반
Hierarchical and Density-based Causal Clustering
Understanding treatment effect heterogeneity is vital for scientific and policy research. However, identifying and evaluating heterogeneous treatment effects pose significant challenges due to the typically unknown subgr…
ClusteringcounterfactualregressionScalable Hierarchical Agglomerative Clustering
The applicability of agglomerative clustering, for inferring both hierarchical and flat clustering, is limited by its scalability. Existing scalable hierarchical clustering methods sacrifice quality for speed and often l…
2D Human Pose EstimationClusteringUsing Machine Learning for Lunar Mineralogy-I: Hyperspectral Imaging of Volcanic Samples
This study examines the mineral composition of volcanic samples similar to lunar materials, focusing on olivine and pyroxene. Using hyperspectral imaging from 400 to 1000 nm, we created data cubes to analyze the reflecta…
ClusteringMulti-Prototypes Convex Merging Based K-Means Clustering Algorithm
K-Means algorithm is a popular clustering method. However, it has two limitations: 1) it gets stuck easily in spurious local minima, and 2) the number of clusters k has to be given a priori. To solve these two issues, a …
ClusteringHierarchical PCA and Modeling Asset Correlations
Modeling cross-sectional correlations between thousands of stocks, across countries and industries, can be challenging. In this paper, we demonstrate the advantages of using Hierarchical Principal Component Analysis (HPC…
Clustering