Work

A DC Optimization Algorithm for Clustering Problems with $𝑳_𝟏$-norm

Year: 2017

Type: article

Abstract: Clustering problems with the similarity measure defined by the 𝐿 1 -norm are studied.Characterizations of different stationary points of these problems are given using their difference of convex representations.An algorithm for finding the Clarke stationary points of the clustering problems is designed and a clustering algorithm is developed based on it.The clustering algorithm finds a center of a data set at the first iteration and gradually adds one cluster center at each consecutive iteration.The proposed algorithm is tested using large real world data sets and compared with other clustering algorithms.

Source: Iranian Journal of Operations Research

DOI: 10.29252/iors.8.2.2

Authors: Adil Bagirov, Sona Taheri

Institution: Federation University

Language: English

Topic: Advanced Clustering Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funder: Australian Research Council

Award: Exploring and exploiting structures in nonsmooth and global optimization problems