Fuzzy C-means Method Research Articles

Model-based analysis of multi-UAV path planning for surveying postdisaster building damage

Emergency responders require accurate and comprehensive data to make informed decisions. Moreover, the data should be acquired and analyzed swiftly to ensure an efficient response. One of the tasks at hand post-disaster is damage assessment within the impacted areas. In particular, building damage should be assessed to account for possible casualties, and displaced populations, to estimate long-term shelter capacities, and to assess the damage to services that depend on essential infrastructure (e.g. hospitals, schools, etc.). Remote sensing techniques, including satellite imagery, can be used to gathering such information so that the overall damage can be assessed. However, specific points of interest among the damaged buildings need higher resolution images and detailed information to assess the damage situation. These areas can be further assessed through unmanned aerial vehicles and 3D model reconstruction. This paper presents a multi-UAV coverage path planning method for the 3D reconstruction of postdisaster damaged buildings. The methodology has been implemented in NetLogo3D, a multi-agent model environment, and tested in a virtual built environment in Unity3D. The proposed method generates camera location points surrounding targeted damaged buildings. These camera location points are filtered to avoid collision and then sorted using the K-means or the Fuzzy C-means methods. After clustering camera location points and allocating these to each UAV unit, a route optimization process is conducted as a multiple traveling salesman problem. Final corrections are made to paths to avoid obstacles and give a resulting path for each UAV that balances the flight distance and time. The paper presents the details of the model and methodologies, and an examination of the texture resolution obtained from the proposed method and the conventional overhead flight with the nadir-looking method used in 3D mappings. The algorithm outperforms the conventional method in terms of the quality of the generated 3D model.

The Ukrainian Economy Transformation into the Circular Based on Fuzzy-Logic Cluster Analysis

In the era of limited resources and progressive environmental degradation, the circular economy is a practical application of sustainable development. It is an alternative, but also competitive way to achieve economic growth in accordance with the principles of sustainable development. This issue was considered in this paper in the context of the Ukrainian economy. The Ukrainian economy’s transformation into a circular one needs to find ways to choose practical tools for such a transition, considering the destructive impact of economic activities on the environment, population, and economy. The goal was to develop a method of choosing tools for the circular transformations of economic activities for each cluster and to reduce man-made damage to the environment. Cluster analysis, fuzzy C-means method, and grouping of economic activities were used. Two analyzed sectors turned out to be the most interesting: mining and quarrying, and electricity, gas, steam, and air conditioning supply, which were finally assigned to the cluster with a high level of destructive impact, defined as ‘environmentally unfriendly’. The proposed method allows the choice of circular transformation tools for economic activities depending on the destructive impact of these economic activities within each cluster.

Morphological Reconstruction-Based Image-Guided Fuzzy Clustering with a Novel Impact Factor.

The guided filter is a novel explicit image filtering method, which implements a smoothing filter on “flat patch” regions and ensures edge preserving on “high variance” regions. Recently, the guided filter has been successfully incorporated into the process of fuzzy c-means (FCM) to boost the clustering results of noisy images. However, the adaptability of the existing guided filter-based FCM methods to different images is deteriorated, as the factor ε of the guided filter is fixed to a scalar. To solve this issue, this paper proposes a new guided filter-based FCM method (IFCM_GF), in which the guidance image of the guided filter is adjusted by a newly defined influence factor ρ. By dynamically changing the impact factor ρ, the IFCM_GF acquires excellent segmentation results on various noisy images. Furthermore, to promote the segmentation accuracy of images with heavy noise and simplify the selection of the influence factor ρ, we further propose a morphological reconstruction-based improved FCM clustering algorithm with guided filter (MRIFCM_GF). In this approach, the original noisy image is reconstructed by the morphological reconstruction (MR) before clustering, and the IFCM_GF is performed on the reconstructed image by utilizing the adjusted guidance image. Due to the efficiency of the MR to remove noise, the MRIFCM_GF achieves better segmentation results than the IFCM_GF on images with heavy noise and the selection of the influence factor for the MRIFCM_GF is simple. Experiments demonstrate the effectiveness of the presented methods.

Grouping Districts/Cities in Kalimantan Island Based on The People's Welfare Indicators Using Fuzzy C-Means and Subtractive Fuzzy C-Means Methods

Cluster analysis has the aim of grouping several objects of observation based on the data found in the information to describe the objects and their relationships. The grouping method used in this research is the Fuzzy C-Means (FCM) and Subtractive Fuzzy C-Means (SFCM) methods. The two grouping methods were applied to the people's welfare indicator data in 42 regencies/cities on the island of Kalimantan. The purpose of this study was to obtain the results of grouping districts/cities on the island of Kalimantan based on indicators of people's welfare and to obtain the results of a comparison of the FCM and SFCM methods. Based on the results of the analysis, the FCM and SFCM methods yield the same conclusions, so that in this study the FCM and SFCM methods are both good to use in classifying districts/cities on the island of Kalimantan based on people's welfare indicators and produce an optimal cluster of two clusters, namely the first cluster consisting of 10 Regencies/Cities on the island of Kalimantan, while the second cluster consists of 32 districts/cities on the island of Borneo.

Research on Multi-objective Trajectory Prediction Algorithm Based on Driving Intent Classification

The trajectory prediction of the multi-object vehicle ahead plays a huge role in improving the vehicle’s driving safety and improving the planning and traffic efficiency of the vehicle. However, due to the uncertainty of the driving intention of the multi-object vehicle and the uncertainty of the vehicle dynamics, its trajectory prediction faces a huge challenge. First, the fuzzy C-means (FCM) method is used for multi-object trajectories. After offline training is carried out using the relevant information of the vehicle trajectory, the driving intention can be automatically fuzzy classification. Secondly, the long short-term memory (LSTM) method uses the history and current trajectory information of multi-object vehicles ahead to predict the future trajectory under different driving intentions. Then, according to the fuzzy classification results of the driving intention, the predicted trajectories are merged, and the trajectory prediction of the multi-objective vehicle ahead in the next 1s is realized through an iterative method. Finally, use real vehicle data for experimental verification. The results show that 92.1% of the lateral distance prediction error in the tenth step is less than 0.13m. The maximum distance prediction error is 0.54m. In the longitudinal distance, the prediction error of 94.5% is less than 0.6m. The maximum distance prediction error is 1.0m.

Clustering and Classification Based on Distributed Automatic Feature Engineering for Customer Segmentation

To beat competition and obtain valuable information, decision-makers must conduct in-depth machine learning or data mining for data analytics. Traditionally, clustering and classification are two common methods used in machine mining. For clustering, data are divided into various groups according to the similarity or common features. On the other hand, classification refers to building a model by given training data, where the target class or label is predicted for the test data. In recent years, many researchers focus on the hybrid of clustering and classification. These techniques have admirable achievements, but there is still room to ameliorate performances, such as distributed process. Therefore, we propose clustering and classification based on distributed automatic feature engineering (AFE) for customer segmentation in this paper. In the proposed algorithm, AFE uses artificial bee colony (ABC) to select valuable features of input data, and then RFM provides the basic data analytics. In AFE, it first initializes the number of cluster k. Moreover, the clustering methods of k-means, Wald method, and fuzzy c-means (FCM) are processed to cluster the examples in variant groups. Finally, the classification method of an improved fuzzy decision tree classifies the target data and generates decision rules for explaining the detail situations. AFE also determines the value of the split number in the improved fuzzy decision tree to increase classification accuracy. The proposed clustering and classification based on automatic feature engineering is distributed, performed in Apache Spark platform. The topic of this paper is about solving the problem of clustering and classification for machine learning. From the results, the corresponding classification accuracy outperforms other approaches. Moreover, we also provide useful strategies and decision rules from data analytics for decision-makers.

Research on grid load model based on SPACK architecture of power big data

Big data service is the key to the construction of smart grid, and the method of analyzing electricity consumption behavior based on improved AP clustering and the method of forecasting electricity load based on random forest is proposed. To address the problem of high complexity of AP clustering analysis, the entropy weight method is used to establish the index weights and improve the similarity calculation method to realize the fast and accurate analysis of customers’ electricity consumption behavior. For the problem of power load prediction, the fuzzy C-means method is used to construct the index weights. The fuzzy C-means is used to construct the historical similarity daily sample set, and the random forest is used to predict the electric load. The SPACK multi-level data processing framework is proposed to meet the needs of parallel processing speed for large amounts of data. The simulation results verify the effectiveness of the method proposed in this article and the important guiding price for the Power grid.

Construction of fuzzy radial basis function neural network model for diagnosing prostate cancer

In this paper, we propose a construction of fuzzy radial basis function neural network model for diagnosing prostate cancer. A fuzzy radial basis function neural network (fuzzy RBFNN) is a hybrid model of logical fuzzy and neural network. The fuzzy membership function of the fuzzy RBFNN model input is developed using the triangle function. The fuzzy C-means method is applied to estimate the center and the width parameters of the radial basis function. The weight estimation is performed by various ways to gain the most accurate model. A singular value decomposition (SVD) is exploited to address this process. As a comparison, we perform other ways including back propagation and global ridge regression. The study also promotes image preprocessing using high frequency emphasis filter (HFEF) and histogram equalization (HE) to enhance the quality of the prostate radiograph. The features of the textural image are extracted using the gray level co-occurrence matrix (GLCM) and gray level run length matrix (GLRLM). The experiment results of fuzzy RBFNN are compared to those of RBFNN model. Generally, the performances of fuzzy RBFNN surpass the RBFNN in all accuracy calculation. In addition, the fuzzy RBFNN-SVD demonstrates the most accurate model for prostate cancer diagnosis.

A Multi-Scale Information Fusion Level Set for Breast Tumor Segmentation

Purpose: Mammography is considered an effective method of examination in early breast cancer screening. Massive work by distinguished researchers of breast segmentation has been proposed. However, due to the blurry boundaries of the breast tumor, the variability of its shape and the overlap with surrounding tissue, the breast tumor’s accurate segmentation still is a challenge. Methods: In this paper, we proposed a novel level set model which based on the optimized local region driven gradient enhanced level set model (OLR-GCV) to segment tumor within a region of interest (ROI) in a mammogram. Firstly, Noise, labels and artifacts are removed from breast images. The ROI is then obtained using the intuitionistic fuzzy C-means method. Finally, we used OLR-GCV method to accurately segment the breast tumor. The OLR-GCV model combines regional information, enhanced edge information and optimized Laplacian of Gaussian (LOG) energy term. The regional and enhanced edge information are used to capture local, global and gradient information of breast images. The optimized Laplacian of Gaussian (LOG) energy term is introduced in the energy functional to further optimize edge information to improve segmentation accuracy. Results: We evaluated our method on the MIAS and DDSM datasets. It yielded a Dice value of 96.86% on the former and 95.51% on the latter. Our method proposed achieves higher accuracy of segmentation than other State-of-the-art Methods. Conclusions: Our method has better segmentation performance, and can be used in clinical practice.

Penerapan Metode Clustering dengan Fuzzy C-Means untuk Memetakan Daerah Rawan Kecelakaan Lalu Lintas di Surakarta

Along with the development of Surakarta's infrastructure, the need for transportation has also increased. Indirectly, it will also cause several problems that must be considered, such as traffic accidents. Data regarding traffic accidents can be used to classify road sections based on the characteristic similarity factor inherent in the data. The sample data used were 1429 accident data from 89 road data in the city of Surakarta. The clustering method used to get the expected results is the Fuzzy C-Means method. The results of accident data grouping are displayed using tables and maps that describe the mapping of road sections in the jurisdiction of the Surakarta City Police. The variables used to cluster data are the number of events, the number of victims who died, and the number of injured victims. The result of this research is a system application that can classify accident-prone areas using the Fuzzy C-Means method into 3 clusters, where the first cluster consists of 5 data, the second cluster consists of 20 data, and the third cluster consists of 64 data.

A neighborhood median weighted fuzzy c-means method for soil pore identification

Complex soil pore geometry and heterogeneity determines the ability of a soil to retain moisture and conduction. These soil properties are widely recognized as key factors of essential ecological functions and services. However, until recently, the existing pore identification methods have the problems of low identification accuracy and operating efficiency, which has restricted the development of soil science. The objective of this study was to propose a neighborhood median weighted fuzzy c-means method based on grayscale-gradient feature (NMFCM-G) to identify soil pore structure automatically and accurately. By combining three-dimensional (3D) printing technology with X-ray computed tomography technology, a 3D simulation model with known aperture (10-cm inner diameter) was adopted to evaluate the pore identification error rate of the NMFCM-G method quantitatively. Compared to the methods commonly applied in previous studies, the NMFCM-G method had the smallest average area relative error (2.98%), which was only 1/6 of that of the Image J method with the largest area relative error (18.46%). The NMFCM-G method also had the smallest average perimeter (5.46%), about 3/5 of that of the Image-Pro Plus method with the largest perimeter relative error (8.35%). Meanwhile, the NMFCM-G method was successfully tested on undisturbed cylindrical soil samples, providing encouraging results in terms of identifying irregular pore structure from the complex hierarchical organization of soil. The results show that the NMFCM-G method had the smallest distribution entropy (0.81), the smallest inter-class correlation (0.164 0), and the largest distribution coefficient (0.11), which proves that the NMFCM-G method performed the best in identifying different soil pore structures. Overall, the NMFCM-G method provides new insights into identifying pore structures and thus could provide an automatic and high-efficiency technique for studying the effects of tillage and freeze-thaw cycles on pore structure and soil quality in the future.

Approximation algorithms for fuzzy C-means problem based on seeding method

As a kind of important soft clustering model, the fuzzy C-means method is widely applied in many fields. In this method, instead of the strict distributive ability in the classical k-means method, all the sample points are endowed with degrees of membership to each center to depict the fuzzy clustering. In this paper, we show that the fuzzy C-means++ algorithm, which introduces the k-means++ algorithm as a seeding strategy, gives a solution for which the approximation guarantee is O(k2ln⁡k). A novel seeding algorithm is then designed based on the contribution of the fuzzy potential function, which improves the approximation ratio to O(kln⁡k). Preliminary numerical experiments are proposed to support the theoretical results of this paper.

Robustness of radiomic features of benign breast lesions and hormone receptor positive/HER2-negative cancers across DCE-MR magnet strengths

Radiomic features extracted from breast lesion images have shown potential in diagnosis and prognosis of breast cancer. As medical centers transition from 1.5 T to 3.0 T magnetic resonance (MR) imaging, it is beneficial to identify potentially robust radiomic features across field strengths because images acquired at different field strengths could be used in machine learning models. Dynamic contrast-enhanced MR images of benign breast lesions and hormone receptor positive/HER2-negative (HR+/HER2-) breast cancers were acquired retrospectively, yielding 612 unique cases: 150 and 99 benign lesions imaged at 1.5 T and 3.0 T, and 223 and 140 HR+/HER2- cancerous lesions imaged at 1.5 T and 3.0 T, respectively. In addition, an independent set of seven lesions imaged at both field strengths, three benign lesions and four HR+/HER2- cancers, was analyzed separately. Lesions were automatically segmented using a 4D fuzzy c-means method; thirty-eight radiomic features were extracted. Feature value distributions were compared by cancer status and imaging field strength using the Kolmogorov-Smirnov test. Features that did not demonstrate a statistically significant difference were considered to be potentially robust. The area under the receiver operating characteristic curve (AUC), for the task of classifying lesions as benign or HR+/HER2- cancer, was determined for each feature at each field strength. Three features were found to be both potentially robust across field strength and of high classification performance, i.e., AUCs statistically greater than 0.5 in the classification task: one shape feature (irregularity), one texture feature (sum average) and one enhancement variance kinetics features (enhancement variance increasing rate). In the demonstration set of lesions imaged at both field strengths, two of the three potentially robust features showed qualitative agreement across field strength. These findings may contribute to the development of computer-aided diagnosis models that are robust across field strength for this classification task.

Optimalisasi Penyaluran Bantuan Pemerintah Untuk UMKM Menggunakan Metode Fuzzy C-Means

Indonesian MSMEs were very seriously affected by the Covid-19 pandemic, which caused the Indonesian economy has experienced deceleration. The Indonesian government has taken several steps to keep economic activity running, such as direct cash assistance for micro-scale businesses but is having problems in obtaining real data so that assistance is not on target, the clustering method using Fuzzy C-Means (FCM) is used for grouping MSME data. FCM allows the data to be a member of all clusters in which each cluster has a membership degree value of 0-1. The data used is from the website of the Sleman Regency Cooperatives and SME Service. FCM classifies MSME data based on the attributes of revenue, assets and number of workers. This research resulted in grouping MSME data into 3 priority levels for MSMEs in obtaining assistance, namely high priority, medium priority, and low priority. The results of this study show that the number of MSMEs with high priority is 23,023 MSMEs, medium priority is 9,774 MSMEs and low priority is 3,159 MSMEs. The validation test of the FCM method uses the Partition Coefficient Index (PCI) which has a value of 0.826 which means that value good because it is close to 1.

Analisis Kinerja Fuzzy C-Means (FCM) dan Fuzzy Subtractive (FS) dalam Clustering Data Alumni STMIK STIKOM Indonesia

In this study, clustering data on STMIK STIKOM Indonesia alumni using the Fuzzy C-Means and Fuzzy Subtractive methods. The method used to test the validity of the cluster is the Modified Partition Coefficient (MPC) and Classification Entropy (CE) index. Clustering is carried out with the aim of finding hidden patterns or information from a fairly large data set, considering that so far the alumni data at STMIK STIKOM Indonesia have not undergone a data mining process. The results of measuring cluster validity using the Modified Partition Coefficient (MPC) and Classification Entropy (CE) index, the Fuzzy C-Means Clustering algorithm has a higher level of validity than the Fuzzy Subtractive Clustering algorithm so it can be said that the Fuzzy C-Means algorithm performs the cluster process better than with the Fuzzy Subtractive method in clustering alumni data. The number of clusters that have the best fitness value / the most optimal number of clusters based on the CE and MPC validity index is 5 clusters. The cluster that has the best characteristics is the 1st cluster which has 514 members (36.82% of the total alumni). With the characteristics of having an average GPA of 3.3617, the average study period is 7.8102 semesters and an average TA work period of 4.9596 months.

A PENERAPAN METODE SUBTRACTIVE FUZZY C-MEANS PADA TINGKAT PARTISIPASI PENDIDIKAN JENJANG SEKOLAH MENENGAH ATAS/SEDERAJAT DI KABUPATEN/KOTA PULAU KALIMANTAN TAHUN 2018

Cluster analysis is a data exploration method uses to obtain hidden characteristics by forming data clusters. One of the cluster analysis methods is Subtractive Fuzzy C-Means (SFCM). SFCM is a combination of Subtractive Clustering and Fuzzy C-Means methods. The SFCM method has the advantages of not requiring many iterations and the results obtained are more stable and accurate than the FCM and SC methods. This study aims to determine the result of clustering on the enrollment rate data for Senior High School (SHS) / equivalent. The data used were the enrollment rate data for high school / equivalent level in the Regency / City of Kalimantan Island in 2018 using three variables, namely the Crude Participation Rate (CPR), the School Participation Rate (SPR) and the Net Enrollment Rate (NER). Based on the three validity indices, namely Partition Coefficient Index (PCI) Validity Index, Modified Partition Coefficient Index (MPCI), and Xie & Beni Index (XBI) in the SFCM method, the optimal cluster were two clusters.
 Keywords: clustering, education, Subtractive Fuzzy C-Means

Segmentation of white matter, grey matter and cerebrospinal fluid from brain MR images using a modified FCM based on double estimation

This paper presents a new fuzzy-based method for the segmentation of brain structures from noisy magnetic resonance (MR) images, in the presence of noise. Our algorithm is a new extension of the fuzzy C-means (FCM) algorithm. The proposed algorithm is developed by modifying the objective function in the FCM using double estimation by incorporating both the original and denoised images in place of using solely the denoised image. To the best of our knowledge, the proposed algorithm is the first extension of the FCM method that is capable of segmenting images (per pixel) based on both noisy and denoised image estimates. In this algorithm we: (a) introduce a novel formulation that assigns weights for each estimation using spatial image information and (b) apply a kernel distance metric for image segmentation. This formulation is highly applicable in segmenting images corrupted by high levels of noise. Experimental results on both simulated and original MR images are presented to demonstrate the robustness and effectiveness of our proposed algorithm in the presence of noise. These results are compared to the nonlocal fuzzy C-means method (LNLFCM), discrete cosine transform-LNLFCM (DCT-LNLFCM), kernel weighted fuzzy local information C-means (KWFLICM), and bias correction embedded fuzzy C-means with spatial constraint (BCEFCM-S) algorithm.

Classification of Deep Sea Sound Speed Profiles in the Northwest Pacific Based on Cluster Analysis

Sound speed profile in ocean has significant seasonal and sea area distribution characteristics. Different sound speed profiles have different effects on underwater acoustic propagation, which makes the detection performance of sonar have significant differences. In order to study the distribution characteristics of sound speed profile in the Northwest Pacific, using WOA13 data set, calculate the number of types by application hierarchical clustering method, using cluster fuzzy c-means method classify sound speed profile, calculate the distribution area of various types in different seasons and typical sound speed profile structure. It is concluded that there are 6 types of sound speed profiles in Northwest pacific: Subarctic category, subarctic transitional category, subtropical transitional category, subtropical category, tropical transitional category and equatorial tropical category. The distribution area of these categories is generally zonal distribution. Except for the tropical transitional category and equatorial tropical category, the other categories have significant seasonal changes.

Prediction of 10-min, hourly, and daily atmospheric air temperature: comparison of LSTM, ANFIS-FCM, and ARMA

Prediction of atmospheric air temperature (AAT) time series is an important issue as it gives information to society and sustainability for future planning. In this study, a deep learning method, namely, long short-term memory (LSTM) network, based on one-step-ahead prediction approach was proposed to predict AAT using the actual time series data. For the proposed prediction method, a set of measurement data in 10-min, hourly, and daily intervals obtained from Mersin and Belen stations located in the Eastern Mediterranean Region of Turkey was used. Mean absolute percentage error (MAPE), root mean square error (RMSE), correlation coefficient (R), mean absolute error (MAE), and average bias were considered as evaluation criteria. According to the testing process, the RMSE, MAPE, MAE, R, and bias values for the 10-min interval AAT prediction were calculated as 0.35 °C, 1.40%, 0.25 °C, 0.995, and 0.074 °C, respectively. Considering the prediction results of the hourly AAT predication, the above statistical metrics with the same order were obtained as 0.61 °C, 1.85%, 0.43 °C, 0.945, and −0.013 °C. Concerning the daily AAT prediction results with LSTM, the above statistical metrics with the same order were computed as 1.33 °C, 3.27%, 0.99 °C, 0.97, and −0.116 °C. Compared to the hourly and daily AAT predictions, LSTM provided better accuracy results in predicting 10-min interval AAT. The prediction results from the three different time series data show that the prediction of AATs using LSTM can provide high accuracy results for short-term prediction using data with a long period time. On the other hand, adaptive neuro-fuzzy inference system with fuzzy C-means (ANFIS-FCM) method and autoregressive moving average (ARMA) model were also used to compare the results of LSTM method. Both LSTM and ANFIS-FCM network model showed high accuracy for the prediction of 10-min interval, hourly, and daily AAT data with RMSE values between 0.31 and 1.52 °C, while ARMA model failed to provide high accuracies for all predictions.

A scalable eigenspace-based fuzzy c-means for topic detection

PurposeThe aim of this research is to develop an eigenspace-based fuzzy c-means method for scalable topic detection.Design/methodology/approachThe eigenspace-based fuzzy c-means (EFCM) combines representation learning and clustering. The textual data are transformed into a lower-dimensional eigenspace using truncated singular value decomposition. Fuzzy c-means is performed on the eigenspace to identify the centroids of each cluster. The topics are provided by transforming back the centroids into the nonnegative subspace of the original space. In this paper, we extend the EFCM method for scalability by using the two approaches, i.e. single-pass and online. We call the developed topic detection methods as oEFCM and spEFCM.FindingsOur simulation shows that both oEFCM and spEFCM methods provide faster running times than EFCM for data sets that do not fit in memory. However, there is a decrease in the average coherence score. For both data sets that fit and do not fit into memory, the oEFCM method provides a tradeoff between running time and coherence score, which is better than spEFCM.Originality/valueThis research produces a scalable topic detection method. Besides this scalability capability, the developed method also provides a faster running time for the data set that fits in memory.