In this paper, we deal with a systematic literature review of different clustering methods and propose a general categorization for them. Furthermore, we compare the performance the methods as well as the related algorithms and their strengths and weaknesses. Finally, we rank the algorithms ...
The evaluation of clustering algorithms is intrinsically difficult because of the lack of objective measures. Since the evaluation of clustering algorithms normally involves multiple criteria, it can be modeled as a multiple criteria decision making (MCDM) problem. This paper presents an MCDM-based appr...
In this article, we evaluate the performance of three clustering algorithms, hard K-Means, single linkage, and a simulated annealing (SA) based technique, in conjunction with four cluster validity indices, namely Davies-Bouldin index, Dunn's index, Calinski-Harabasz index, and a recently developed...
In this paper, we explore the applicability of K-means and Fuzzy C-Meansclustering algorithms to student allocation problem that allocates new studentsto homogenous groups of specified maximum capacity, and analyze effects of suchallocations on the academic performance of students. The paper also ...
Single-Cell RNA-seq Cancer Clustering Framework Automated algorithms 1. Introduction Tumors are composed of complex subpopulations of varying cell types, including but not limited to neoplastic cells, stromal fibroblasts, endothelial and immune cells [1], [2], [3], [4]. This can be the result...
Our main goal is to evaluate whether or not clustering-based techniques can compete in efficiency and effectiveness against the most studied state-of-the-art algorithms in the literature. We consider the quality of the results, the resilience against different types of data and variations in ...
There are various functions with the help of which we can evaluate the performance of clustering algorithms.Following are some important and mostly used functions given by the Scikit-learn for evaluating clustering performance −Adjusted Rand Index...
Properties specific for the clustering algorithms benchmarking: Synthetic networks generation with the extendedLFR Frameworkand subsequent shuffling (network nodes and links reordering); Execution of theclustering algorithmson the specified networks;
clusteringstabilityclusterfeaturea hierarchical groupingvarianceCurrently, there are many clustering algorithms for the case of a known/unknown number of clusters. Typically, clustering is a result of optimisation of some quality criterion or iterative process. How to estimate the quality of clustering ...
Omega Index evaluation is pretty slow here, usexmeasuresfor the order of magnitude faster evaluation. Internal note:the counter example for this measure (onmi) isrotshift_single.cnl. The paper:"Normalized Mutual Information to evaluate overlapping community finding algorithms"by Aaron F. McDaid, ...