A comparative analysis of selected clustering algorithms for criminal profiling

The proper classification of pixels is an important step in the realm of satellite imagery, to partition different land cover regions. This paper describes a clustering method that utilizes hard and fuzzy clustering algorithms. The performance of the algorithm is optimized using genetic algorithm, which searches the best cluster centers to initialize the fuzzy partition matrix in place of random initialization. The proposed approach provides accurate clustering results for gray-level images. Comparison between segmentation results of hard c-means, fuzzy c-means and fuzzy c-means genetic algorithm (FGA) is presented.

Clustering of multi-view relational data based on particle swarm optimization

Use of a fuzzy granulation–degranulation criterion for assessing cluster validity

Clustering is an important unsupervised machine learning techniqueused in diverse fields to explore the inherent structure of the data. In most of the real life datasets, one object resides in many clusters with different membership values. Many clustering algorithms have been proposed for finding such overlapping clusters for knowledge extraction and future trend prediction. In the paper, multi-objective genetic algorithm based cluster analysis technique is proposed for finding the optimal set of overlapping clusters. As most of the real world search and optimization problems involve multiple objectives, multi-objective Genetic Algorithm is an obvious choice for capturing multiple optimal solutions. Thus the usefulness of applying the multi-objective Genetic Algorithm is to grouping the objects based on different objective functions for finding optimal set of overlapping clusters. The advantage of this algorithm is that it assigns a membership value only to the objects which are the members of several clusters, instead of assigning membership values for all clusters like fuzzy clustering algorithm. If any object positively belongs only to a single cluster, its membership value for this cluster is ' 1' and '0' for all other clusters. The overall performance of the method is investigated on some popular UCI and microarray datasets and the optimality of the clusters is measured by some important cluster validation indices. The experimental results show the effectiveness of the proposed method.

Image segmentation plays an important role in machine vision, image recognition, and imaging applications. Based on the fuzzy c-means clustering algorithm, a kernel-distance-based intuitionistic fuzzy c-means clustering (KIFCM) algorithm is proposed. First, a fuzzy complement operator is used to generate the membership degree whereby the hesitation degree of intuitionistic fuzzy set is generated; second, a kernel-induced function is used to calculate the distance from each point to the cluster center instead of the Euclidean distance; third, a new objective function that includes the hesitation degree is established, and the optimization of the objective function results in new iterative expressions for the membership degree and the cluster center. The proposed KIFCM algorithm is compared with the fuzzy c-means clustering (FCM) algorithm, the kernel fuzzy c-means clustering (KFCM) algorithm, and the intuitionistic fuzzy c-means clustering (IFCM) algorithm in segmenting five images. The experimental results verify the effectiveness and superiority of our proposed KIFCM algorithm.

On convergence and parameter selection of the EM and DA-EM algorithms for Gaussian mixtures

This work presents a novel strategy for classifying neurons, represented by nodes of a directed graph, based on their circuitry (edge connectivity). We assume a stochastic block model (SBM) in which neurons belong together if they connect to neurons of other groups according to the same probability distributions. Following adjacency spectral embedding of the SBM graph, we derive the number of classes and assign each neuron to a class with a Gaussian mixture model-based expectation maximization (EM) clustering algorithm. To improve accuracy, we introduce a simple variation using random hierarchical agglomerative clustering to initialize the EM algorithm and picking the best solution over multiple EM restarts. We test this procedure on a large (≈212–215 neurons), sparse, biologically inspired connectome with eight neuron classes. The simulation results demonstrate that the proposed approach is broadly stable to the choice of embedding dimension, and scales extremely well as the number of neurons in the network increases. Clustering accuracy is robust to variations in model parameters and highly tolerant to simulated experimental noise, achieving perfect classifications with up to 40% of swapped edges. Thus, this approach may be useful to analyze and interpret large-scale brain connectomics data in terms of underlying cellular components.

The localization accuracy of a fingerprint-based localization system is dependent on several factors, one of which is the accuracy and efficiency at which the fingerprint database is clustered. Most highly efficient and accurate clustering algorithms have high time-dependent computational complexity (CC), which tends to limit their practical applicability. A technique that has yet to be explored is the sequential hybridization of multiple low-time CC clustering algorithms to produce a single moderate-time CC clustering algorithm with high localization accuracy. As a result, this paper proposes a clustering algorithm with a moderate time CC that is based on the sequential hybridization of the closest access point (CAP) and improved k-means clustering algorithms. The performance of the proposed sequential hybrid clustering algorithm is determined and compared to the modified affinity propagation clustering (m-APC), fuzzy c-mean (FCM), and 2-CAP algorithms presented in earlier research works using four experimentally generated and publicly available fingerprint databases. The performance metrics considered for the comparisons are the position root mean square error (RMSE) and clustering time based on big O notation. The simulation results show that the proposed sequential hybrid clustering algorithm has improved localization accuracy with position RMSEs of about 54%, 77%, and 52%, respectively, higher than those of the m-APC, FCM, and 2-CAP algorithms. In terms of clustering time, it is 99% and 79% faster than the m-APC and FCM algorithms, respectively, but 90% slower than the 2-CAP algorithm. The results have shown that it is possible to develop a clustering algorithm that has a moderate clustering time with very high localization accuracy through sequential hybridization of multiple clustering algorithms that have a low clustering time with poor localization accuracy. Doi: 10.28991/ESJ-2024-08-02-02 Full Text: PDF

Many components of intelligent transportation systems require different levels of the traffic flow forecasting. This paper presents a novel short-term traffic flow forecasting model using a distributed radial basis function neural network (RBFNN) based on adaptive fuzzy c-means (FCM) clustering algorithm. FCM clustering algorithm is used to classify training objects into a couple of clusters, each cluster is trained by a sub RBFNN, and membership values are used for combining several RBFNN outputs to obtain the final result. In the online stage, the membership values are computed using an adaptive fuzzy clustering algorithm for the new object. The real traffic data are used to demonstrate the effectiveness of the method.

Possibilistic fuzzy c-means clustering (PFCM) algorithm improves the noise sensitivity of the fuzzy c-means clustering (FCM) algorithm and the serious coincident clustering phenomenon of the possibilistic c-means clustering (PCM) algorithm. However, the Euclidean distance of the PFCM treats all features equally in the clustering process and doesn't consider the imbalance between sample features, resulting in low clustering accuracy. Furthermore, as the number of clusters increases, the PFCM still has a partially coincident clustering problem due to the lack of between-class relationships of possibilistic memberships. Therefore, a feature weighted cutset-type possibilistic fuzzy c-means clustering (FW-C-PFCM) algorithm is proposed by introducing the cutset theory and feature weights into the PFCM in this paper. Firstly, the proposed FW-C-PFCM introduces a feature weighted parameter in the objective function, and different features should take different weight values according to the distribution of samples. Secondly, the cutset theory is utilized to divide the data into inner core and outer core regions, and some possibilistic memberships inside each cluster core are modified in the iterative process. Finally, the experiments on synthetic multi-class datasets and the Iris dataset show that the FW-C-PFCM improves the partially coincident clustering phenomenon and overcomes the imbalance between sample features. The comparative experiments on a color image also indicate that the FW-C-PFCM enhances the segmentation accuracy and generates better clustering results than PFCM.

Over the years, research on fuzzy clustering algorithms has attracted the attention of many researchers, and they have been applied to various areas, such as image segmentation and data clustering. Various fuzzy clustering algorithms have been put forward based on the initial Fuzzy C-Means clustering (FCM) with Euclidean distance. However, the existing fuzzy clustering approaches ignore two problems. Firstly, clustering algorithms based on Euclidean distance have a high error rate, and are more sensitive to noise and outliers. Secondly, the parameters of the fuzzy clustering algorithms are hard to determine. In practice, they are often determined by the user’s experience, which results in poor performance of the clustering algorithm. Therefore, considering the above deficiencies, this paper proposes a novel fuzzy clustering algorithm by combining the Gaussian kernel function and Grey Wolf Optimizer (GWO), called Kernel-based Picture Fuzzy C-Means clustering with Grey Wolf Optimizer (KPFCM-GWO). In KPFCM-GWO, the Gaussian kernel function is used as a symmetrical measure of distance between data points and cluster centers, and the GWO is utilized to determine the parameter values of PFCM. To verify the validity of KPFCM-GWO, a comparative study was conducted. The experimental results indicate that KPFCM-GWO outperforms other clustering methods, and the improvement of KPFCM-GWO is mainly attributed to the combination of the Gaussian kernel function and the parameter optimization capability of the GWO. What is more, the paper applies KPFCM-GWO to analyzes the value of an airline’s customers, and five levels of customer categories are defined.

Forecasting activities play an important role in our daily life. In recent years, fuzzy time series (FTS) methods were developed to deal with forecasting problems. FTS attracted researchers because of its ability to predict the future values in some critical situations where most standard forecasting models are doubtfully applicable or produce bad fittings. However, some critical issues in FTS are still open; these issues are often subjective and affect the accuracy of forecasting. In this paper, we focus on improving the accuracy of FTS forecasting methods. The new method integrates the fuzzy clustering and genetic algorithm with FTS to reduce subjectivity and improve its accuracy. In the new method, the genetic algorithm is responsible for selecting the proper model. Also, the fuzzy clustering algorithm is responsible for fuzzifying the historical data, based on its membership degrees to each cluster, and using these memberships to defuzzify the results. This method provides better forecasting accuracy when compared with other extant researches.

To solve the problem of fuzzy identification of nonlinear systems, a novel fuzzy identification method based on fuzzy c-means clustering (FCM) algorithm and Gaussian function is proposed. Firstly, fuzzy clustering algorithm is used to divide the input space to obtain the clustering center, then the clustering center is used as the gaussian function center to determine the membership function to obtain the premise parameters of the fuzzy model, and the conclusion parameters of the fuzzy model are identified by recursive least squares (RLS). Finally, three simulation examples are given to verify the effectiveness of the proposed method in identifying T-S fuzzy model.

The storage and calculation of the larger amount of data in the cloud data center brings more abnormal data. In order to accurately and efficiently find anomalies in the data in the cloud data center, this paper proposes an anomaly detection parameter optimization algorithm for data center data. Methods Fuzzy C-means clustering (FCM) algorithm is used for anomaly detection. At the same time, in order to overcome the problem that the fuzzy clustering algorithm is sensitive to the initial value and easy to fall into the local optimum, the adaptive bat algorithm is used to optimize the FCM algorithm. And add distributed entropy and average distance to the algorithm, adaptively adjust the optimization ability of traditional bat algorithm. Finally, experiments prove the detection accuracy of the algorithm.

There is a trend in recent machine learning community to construct a nonlinear version of a linear algorithm using the 'kernel method', e.g. Support Vector Machines (SVMs), kernel principal component analysis, kernel fisher discriminant analysis and the recent kernel clustering algorithms. In unsupervised clustering algorithms using kernel method, typically, a nonlinear mapping is used first to map the data into a potentially much higher feature space, where clustering is then performed. A drawback of these kernel clustering algorithms is thatthe clustering prototypes lie in high dimensional feature space and hence lack clear and intuitive descriptions unless using additional projection approximation from the feature to the data space as done in the existing literatures. In this paper, a novel clustering algorithm using the 'kernel method' based on the classical fuzzy clustering algorithm (FCM) is proposed and called as kernel fuzzy c-means algorithm (KFCM). KFCM adopts a new kernel-induced metric in the data space to replace the original Euclidean norm metric in FCM and the clustered prototypes still lie in the data space so that the clustering results can be reformulated and interpreted in the original space. Our analysis shows that KFCM is robust to noise and outliers and also tolerates unequal sized clusters. And finally this property is utilized to cluster incomplete data. Experiments on two artificial and one real datasets show that KFCM hasbetter clustering performance and more robust than several modifications of FCM for incomplete data clustering.