Conventional K-means Clustering Research Articles

A hybrid genetic-fuzzy ant colony optimization algorithm for automatic K-means clustering in urban global positioning system

This paper introduces an innovative automatic K-means clustering algorithm, namely HGA-FACO, which seamlessly integrates the noise algorithm, Genetic Algorithm (GA), Ant Colony Optimization (ACO), and Adaptive Fuzzy System (AFS). The rationale behind the HGA-FACO algorithm is to mitigate the shortcomings of traditional K-means, particularly the reliance on pre-determined cluster centers and the need for specifying the number of clusters in advance. By optimizing the search strategy, HGA-FACO efficiently circumvents local optima and effectively explores the global optimal solution, resulting in more accurate and stable clustering outcomes. To validate the superiority of the HGA-FACO over conventional K-Means Clustering (KMeans) and other intelligent clustering approaches such as ACO-KMeans, GA-KMeans (GAK), particle swarm optimization KMeans (PSOK), and ACO-GAK, we conducted comprehensive experiments on taxi Global Positioning System (GPS) datasets sourced from four distinct cities. Employing rigorous evaluation metrics including Silhouette Coefficient (SC), Partition Coefficient (PBM), Davies-Bouldin Index (DBI), and Sum of Squared Errors (SSE), the experimental results convincingly demonstrate that the HGA-FACO significantly outperforms its counterparts across all metrics, highlighting its exceptional performance in clustering effectiveness and compactness. While the HGA-FACO faces challenges related to computational complexity and the necessity for initial parameter tuning, its performance limitations on small-sized or unevenly distributed datasets are acknowledged. Nevertheless, the algorithm's advancements in the field of clustering algorithms are undeniable and hold immense potential for practical applications, notably in city hotspot identification.

Mutated Aquila Optimizer for assisting brain tumor segmentation

Image processing is a crucial effort to discover suspicious regions and robust features from magnetic resonance imaging (MRI). Many segmentation algorithms have been proposed to increase the accuracy of brain tumor detection. However, brain image segmentation is considered a complex and challenging step in medical image processing. K-means is an unsupervised learning method for MRI image clustering and other tasks such as image segmentation and pattern identification of interest. However, the K-means algorithm still has many shortcomings, including a low convergence rate, a tendency to get stuck in local minima, and sensitivity to initialization. An improved version of Aquila Optimizer (AO) boosted with an alternative selection scheme and Cauchy mutation, called Mutated Aquila Optimizer (MAO), and the K-Means algorithm were combined to create a new dynamic and intelligent clustering method for brain tumor segmentation. The proposed MAO-Kmeans approach aims to increase the ability of K-Means to automatically extract the correct number and location of cluster centers and the number of pixels in each cluster in abnormal MRI images (multiple sclerosis lesions). The experimental results on the Brain Tumor Segmentation dataset (BraTS 2020) demonstrated the effectiveness of the MAO-Kmeans method in improving the performance of conventional K-means clustering. Furthermore, qualitative and quantitative comparisons between MAO-Kmeans and the other 12 state-of-the-art segmentation techniques (which include machine learning, artificial neural network, and deep learning-based methods) demonstrate the superiority of our proposed algorithm.

Effective k-Means Clustering in Greedy Prepruned Tree-based Classification for Obstructive Sleep Apnea

Incorporation of prepruned decision trees to k-means clustering through one to three types of tree-depth controllers and cluster partitioning was done to develop a combined algorithm named as Greedy Pre-pruned Tree-based Clustering (GPrTC) algorithm. Pre-pruned clustered decision trees are applied in a greedy concerted way to five datasets of obstructive sleep apnea and others from online data repositories. The optimal number of k clusters for k-means clustering is determined after trees are greedily prepruned by tree-depth controllers of minimum number of leaf nodes, minimum number of parent nodes and maximum number of tree splitting. After applying the GPrTC algorithm to the assigned datasets, when compared with the conventional k-means clustering, results showed that the former has significantly lower average distortion per point and lower average run-time for 2-D and 3-D data over around 30 thousand points. Classification efficiency and speed of the former algorithm is more than two times better the latter algorithm over a higher range of points being run. GPrTC algorithm showed better classification accuracies than k-means clustering in almost all the assigned datasets. This concludes that the proposed algorithm is significantly much more efficient, less distortion and much faster than k-means clustering with moderately better in terms of classification and/or prediction accuracies.

Modulation Classification of MFSK Modulated Signals Using Spectral Centroid

This study utilizes higher order spectrum in order to achieve satisfactory probability of correct classification of M-ary Frequency Shift Keying (MFSK) modulated signals even at low signal to noise ratios. MFSK modulated signals are characterized by a single feature, spectral centroid, which is defined as the centroid value of the diagonal vector of bispectrum matrix. It is observed that conventional K-means clustering is sufficient to achieve satisfactory modulation classification performance using this single feature. The parameters such as bandwidth and chosen FFT size which affect the correct classification ratio at a certain signal to noise ratio are analysed in order to optimize the performance of the proposed method.

Development of new seed with modified validity measures for k-means clustering

Conventional k-means clustering is the widely used partitional method, mainly adapted to machine learning and pattern recognition problems. This algorithm is highly sensitive to initial centroid points, but it cannot guarantee to arrive at a better solution because initial centroids are computed randomly for the given cluster. In this paper, we have developed a new initialization method for k-means clustering. We have also made an effort to improve the Dunn Index and introduced a new validity ratio based on the silhouette index. The sum of squared error, Dunn Index, silhouette index, modified Dunn Index, and silhouette validity ratio were used as criteria to evaluate the performance of the initialization algorithm. Various benchmark datasets have been used to assess the effectiveness of the proposed initialization algorithm, and we compared the results with conventional k-means and k-means++ algorithms. The results have shown that the sum of squared error and number of iterations obtained by our proposed initialization algorithm are minimum. A precision chart is used to test the consistency of the initialization algorithm. The comparative analysis, based on the modified Dunn Index, and silhouette validity ratio have proved that the proposed initialization algorithm has performed better than the other initialization algorithms.

A Blind Nonlinearity Compensator Using DBSCAN Clustering for Coherent Optical Transmission Systems

Coherent fiber-optic communication systems are limited by the Kerr-induced nonlinearity. Benchmark optical and digital nonlinearity compensation techniques are typically complex and tackle deterministic-induced nonlinearities. However, these techniques ignore the impact of stochastic nonlinear distortions in the network, such as the interaction of fiber nonlinearity with amplified spontaneous emission from optical amplification. Unsupervised machine learning clustering (e.g., K-means) has recently been proposed as a practical approach to the blind compensation of stochastic and deterministic nonlinear distortions. In this work, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is employed, for the first time, for blind nonlinearity compensation. DBSCAN is tested experimentally in a 40 Gb/s 16 quadrature amplitude-modulated system at 50 km of standard single-mode fiber transmission. It is shown that at high launched optical powers, DBSCAN can offer up to 0.83 and 8.84 dB enhancement in Q-factor when compared to conventional K-means clustering and linear equalisation, respectively.

Improving K-means clustering with enhanced Firefly Algorithms

In this research, we propose two variants of the Firefly Algorithm (FA), namely inward intensified exploration FA (IIEFA) and compound intensified exploration FA (CIEFA), for undertaking the obstinate problems of initialization sensitivity and local optima traps of the K-means clustering model. To enhance the capability of both exploitation and exploration, matrix-based search parameters and dispersing mechanisms are incorporated into the two proposed FA models. We first replace the attractiveness coefficient with a randomized control matrix in the IIEFA model to release the FA from the constraints of biological law, as the exploitation capability in the neighbourhood is elevated from a one-dimensional to multi-dimensional search mechanism with enhanced diversity in search scopes, scales, and directions. Besides that, we employ a dispersing mechanism in the second CIEFA model to dispatch fireflies with high similarities to new positions out of the close neighbourhood to perform global exploration. This dispersing mechanism ensures sufficient variance between fireflies in comparison to increase search efficiency. The ALL-IDB2 database, a skin lesion data set, and a total of 15 UCI data sets are employed to evaluate efficiency of the proposed FA models on clustering tasks. The minimum Redundancy Maximum Relevance (mRMR)-based feature selection method is also adopted to reduce feature dimensionality. The empirical results indicate that the proposed FA models demonstrate statistically significant superiority in both distance and performance measures for clustering tasks in comparison with conventional K-means clustering, five classical search methods, and five advanced FA variants.

K-polytopes: a superproblem of k-means

It has already been proven that under certain circumstances dictionary learning for sparse representations is equivalent to conventional k-means clustering. Through additional modifications on sparse representations, it is possible to generalize the notion of centroids to higher orders. In a related algorithm which is called k-flats, q-dimensional flats have been considered as alternative central prototypes. In the proposed formulation of this paper, central prototypes are instead simplexes or even more general polytopes. Using higher-dimensional, nonconvex prototypes may alleviate the curse of dimensionality while also enabling to model nonlinearly distributed datasets successfully. The proposed framework in this study can further be applied in supervised settings flexibly through one-class learning and also in other nonlinear frameworks through kernels.

Blind Detection for Spatial Modulation Systems Based on Clustering

In this letter, we propose a pair of blind detectors for spatial modulation multiple-input multiple-output (MIMO) systems based on the clustering concept. Specifically, an improved K-means clustering (KMC) with lower-complexity detector is first proposed to avoid the error floor effects and to reduce the imposed complexity of the conventional KMC detector by employing an initial centroid optimizer. Furthermore, an affinity propagation (AP) detector is developed based on the belief propagation concept, where the number of clusters is not required in the clustering process by using the intelligence information exchanges. The results show that the proposed KMC detector can efficiently avoid the occurrence of error floor effects, while the proposed AP detector is capable of achieving better performance than that of the conventional KMC detector.

Using a Method Based on a Modified K-Means Clustering and Mean Shift Segmentation to Reduce File Sizes and Detect Brain Tumors from Magnetic Resonance (MRI) Images

In this paper, we propose a method of elaborating and detecting brain tumor from MRI suitable for information sharing via the internet for a healthcare provider. This method allows for reducing image sizes without reducing the information content of the images in terms of detecting tumors. The proposed method involves first clarifying the brain tumor area using a modified K-means clustering method and initial segmentation using mean shift segmentation. Then a threshold setting is used to convert the gray scale image and remove noise by applying an erode operation. Finally, the brain tumors in the images are detected using a watershed algorithm. The proposed method was compared with two well-known methods namely the conventional K-mean clustering and Fuzzy C Means (FCM) clustering. We verified the precision and the objectivity of our proposed method. The average precision and recall for our proposed method were excellent with values of 0.914052 and 0.995641, respectively. Our method detected more brain tumors than the conventional K-means clustering and FCM clustering methods and was able to provide for an efficient image data processing with reduced file sizes.

Blind source separation by weighted K-means clustering *

Blind separation of sparse sources (BSSS) is discussed. The BSSS method based on the conventional K-means clustering is very fast and is also easy to implement. However, the accuracy of this method is generally not satisfactory. The contribution of the vector x( t) with different modules is theoretically proved to be unequal, and a weighted K-means clustering method is proposed on this grounds. The proposed algorithm is not only as fast as the conventional K-means clustering method, but can also achieve considerably accurate results, which is demonstrated by numerical experiments.