Many metaheuristic algorithms have recently been reported in addition to the above-discussed algorithms for numerical and real-world engineering design optimization problems, including data clustering. For instance, ant colony optimization35, firefly algorithm36,37, flower pollination algorithm38, grey wolf...
基于差分进化算法的聚类:A Differential Evolution Algorithm with Ada Niching and K-Means Op for Data Clustering Freeman449 2 人赞同了该文章 概述 通过最小化类内方差实现聚类是一个NP难的问题,因此,有作者提出使用进化计算来解决聚类问题。早期基于传统进化算法的方法具有计算复杂度高的问题,同时容易受困于局部...
This article should be useful for the beginning data scientists, or for experts who want to refresh their memories on the topic. It includes the most widespread clustering algorithms, as well as their insightful review. Depending on the particularities of each method, the recommendations considering ...
A NOVEL EVOLUTIONARY ALGORITHM FOR DATA CLUSTERING IN N DIMENSIONAL SPACE K-means clustering algorithm is one of the main algorithms applying in machine learning and pattern recognition. However, as the center of clusters are selected randomly and also due to the dependence of clustering result on ...
The newly proposed clustering algorithm, known as GFA_R, is then tested on a benchmark dataset obtained from the 20Newsgroups. Experimental results on external and relative quality metrics for the GFA_R is compared against the one obtained using the standard GFA and Bisect K-means.It is ...
An important application of graph partitioning is data clustering using a graph model - the pairwise similarities between all data objects form a weighted graph adjacency matrix that contains all necessary information for clustering. In this paper, we propose a new algorithm for graph partitioning wit...
Because, for every different run of the algorithm on the same dataset, you may choose different set of initial centers. This may lead to different clustering results on different runs of the algorithm. It’s sensitive to outliers. If you rearrange your data, it’s very possible that you’ll...
For example, to perform clustering using ellipsoid volume taking into account direction change, where cluster direction is determined using PCA, one would do:idx = clusterdata_mvidc(X, k, idx_init, 'volume', 'ellipsoid', 'dirweight',0.5, 'dirpower', 4, 'dirtype', 'pca'); ...
To address these problems, we propose a Domain-Adaptive Density Clustering (DADC) algorithm, which consists of three steps: domain-adaptive density measurement, cluster center self-identification, and cluster self-ensemble. For data with VDD features, clusters in sparse regions are often neglected by...
a clustering method aiming at identifying ecologically significant haplotypes of bacterial strains. Despite different objectives and parameters, the overall processing strategy of the dbotu3 algorithm is similar to LULU. To validate the LULU algorithm, we used a plant data set forITS2(nuclear ribosomal...