K-expectiles clustering

by   Bingling Wang, et al.

K-means clustering is one of the most widely-used partitioning algorithm in cluster analysis due to its simplicity and computational efficiency. However, K-means does not provide an appropriate clustering result when applying to data with non-spherically shaped clusters. We propose a novel partitioning clustering algorithm based on expectiles. The cluster centers are defined as multivariate expectiles and clusters are searched via a greedy algorithm by minimizing the within cluster 'τ -variance'. We suggest two schemes: fixed τ clustering, and adaptive τ clustering. Validated by simulation results, this method beats both K-means and spectral clustering on data with asymmetric shaped clusters, or clusters with a complicated structure, including asymmetric normal, beta, skewed t and F distributed clusters. Applications of adaptive τ clustering on crypto-currency (CC) market data are provided. One finds that the expectiles clusters of CC markets show the phenomena of an institutional investors dominated market. The second application is on image segmentation. compared to other center based clustering methods, the adaptive τ cluster centers of pixel data can better capture and describe the features of an image. The fixed τ clustering brings more flexibility on segmentation with a decent accuracy.



There are no comments yet.


page 17

page 18


Data Clustering and Graph Partitioning via Simulated Mixing

Spectral clustering approaches have led to well-accepted algorithms for ...

Non-parametric Power-law Data Clustering

It has always been a great challenge for clustering algorithms to automa...

Fast and Interpretable Consensus Clustering via Minipatch Learning

Consensus clustering has been widely used in bioinformatics and other ap...

A New Parallel Adaptive Clustering and its Application to Streaming Data

This paper presents a parallel adaptive clustering (PAC) algorithm to au...

k is the Magic Number -- Inferring the Number of Clusters Through Nonparametric Concentration Inequalities

Most convex and nonconvex clustering algorithms come with one crucial pa...

A Greedy and Optimistic Approach to Clustering with a Specified Uncertainty of Covariates

In this study, we examine a clustering problem in which the covariates o...

Clustering by connection center evolution

The determination of cluster centers generally depends on the scale that...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.