Performance evaluation results of evolutionary clustering algorithm star
for clustering heterogeneous datasets
- URL: http://arxiv.org/abs/2105.02810v1
- Date: Fri, 30 Apr 2021 08:17:19 GMT
- Title: Performance evaluation results of evolutionary clustering algorithm star
for clustering heterogeneous datasets
- Authors: Bryar A. Hassan, TarikA. Rashid, Seyedali Mirjalili
- Abstract summary: This article presents the data used to evaluate the performance of evolutionary clustering algorithm star (ECA*)
Two experimental methods are employed to examine the performance of ECA* against five traditional and modern clustering algorithms.
- Score: 15.154538450706474
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: This article presents the data used to evaluate the performance of
evolutionary clustering algorithm star (ECA*) compared to five traditional and
modern clustering algorithms. Two experimental methods are employed to examine
the performance of ECA* against genetic algorithm for clustering++
(GENCLUST++), learning vector quantisation (LVQ) , expectation maximisation
(EM) , K-means++ (KM++) and K-means (KM). These algorithms are applied to 32
heterogenous and multi-featured datasets to determine which one performs well
on the three tests. For one, ther paper examines the efficiency of ECA* in
contradiction of its corresponding algorithms using clustering evaluation
measures. These validation criteria are objective function and cluster quality
measures. For another, it suggests a performance rating framework to measurethe
the performance sensitivity of these algorithms on varos dataset features
(cluster dimensionality, number of clusters, cluster overlap, cluster shape and
cluster structure). The contributions of these experiments are two-folds: (i)
ECA* exceeds its counterpart aloriths in ability to find out the right cluster
number; (ii) ECA* is less sensitive towards dataset features compared to its
competitive techniques. Nonetheless, the results of the experiments performed
demonstrate some limitations in the ECA*: (i) ECA* is not fully applied based
on the premise that no prior knowledge exists; (ii) Adapting and utilising ECA*
on several real applications has not been achieved yet.
Related papers
- A3S: A General Active Clustering Method with Pairwise Constraints [66.74627463101837]
A3S features strategic active clustering adjustment on the initial cluster result, which is obtained by an adaptive clustering algorithm.
In extensive experiments across diverse real-world datasets, A3S achieves desired results with significantly fewer human queries.
arXiv Detail & Related papers (2024-07-14T13:37:03Z) - GCC: Generative Calibration Clustering [55.44944397168619]
We propose a novel Generative Clustering (GCC) method to incorporate feature learning and augmentation into clustering procedure.
First, we develop a discrimirative feature alignment mechanism to discover intrinsic relationship across real and generated samples.
Second, we design a self-supervised metric learning to generate more reliable cluster assignment.
arXiv Detail & Related papers (2024-04-14T01:51:11Z) - Fuzzy K-Means Clustering without Cluster Centroids [79.19713746387337]
Fuzzy K-Means clustering is a critical computation technique in unsupervised data analysis.
This paper proposes a novel Fuzzy K-Means clustering algorithm that entirely eliminates the reliance on cluster centroids.
arXiv Detail & Related papers (2024-04-07T12:25:03Z) - Instance-Optimal Cluster Recovery in the Labeled Stochastic Block Model [79.46465138631592]
We devise an efficient algorithm that recovers clusters using the observed labels.
We present Instance-Adaptive Clustering (IAC), the first algorithm whose performance matches these lower bounds both in expectation and with high probability.
arXiv Detail & Related papers (2023-06-18T08:46:06Z) - Rethinking k-means from manifold learning perspective [122.38667613245151]
We present a new clustering algorithm which directly detects clusters of data without mean estimation.
Specifically, we construct distance matrix between data points by Butterworth filter.
To well exploit the complementary information embedded in different views, we leverage the tensor Schatten p-norm regularization.
arXiv Detail & Related papers (2023-05-12T03:01:41Z) - Comparison of Clustering Algorithms for Statistical Features of
Vibration Data Sets [0.4806505912512235]
We present an extensive comparison of the clustering algorithms K-means clustering, OPTICS, and Gaussian mixture model clustering (GMM) applied to statistical features extracted from the time and frequency domains of vibration data sets.
Our work showed that averaging (Mean, Median) and variance-based features (Standard Deviation, Interquartile Range) performed significantly better than shape-based features (Skewness, Kurtosis)
With an increase in the specified number of clusters, clustering algorithms performed better, although there were some specific algorithmic restrictions.
arXiv Detail & Related papers (2023-05-11T12:19:30Z) - A Novel Cluster Detection of COVID-19 Patients and Medical Disease
Conditions Using Improved Evolutionary Clustering Algorithm Star [0.9990687944474739]
We improve the current evolutionary clustering algorithm star (ECA*), called iECA*, in three manners.
Experiments were conducted to examine the performance of iECA* against state-of-the-art algorithms.
arXiv Detail & Related papers (2021-09-20T12:47:09Z) - HAWKS: Evolving Challenging Benchmark Sets for Cluster Analysis [2.5329716878122404]
Comprehensive benchmarking of clustering algorithms is difficult.
There is no consensus regarding the best practice for rigorous benchmarking.
We demonstrate the important role evolutionary algorithms play to support flexible generation of such benchmarks.
arXiv Detail & Related papers (2021-02-13T15:01:34Z) - A Multi-disciplinary Ensemble Algorithm for Clustering Heterogeneous
Datasets [0.76146285961466]
We propose a new evolutionary clustering algorithm (ECAStar) based on social class ranking and meta-heuristic algorithms.
ECAStar is integrated with recombinational evolutionary operators, Levy flight optimisation, and some statistical techniques.
Experiments are conducted to evaluate the ECAStar against five conventional approaches.
arXiv Detail & Related papers (2021-01-01T07:20:50Z) - Scalable Hierarchical Agglomerative Clustering [65.66407726145619]
Existing scalable hierarchical clustering methods sacrifice quality for speed.
We present a scalable, agglomerative method for hierarchical clustering that does not sacrifice quality and scales to billions of data points.
arXiv Detail & Related papers (2020-10-22T15:58:35Z) - Simple and Scalable Sparse k-means Clustering via Feature Ranking [14.839931533868176]
We propose a novel framework for sparse k-means clustering that is intuitive, simple to implement, and competitive with state-of-the-art algorithms.
Our core method readily generalizes to several task-specific algorithms such as clustering on subsets of attributes and in partially observed data settings.
arXiv Detail & Related papers (2020-02-20T02:41:02Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.