Brazilian Forest Fire Analysis: An Unsupervised Approach

In this paper the K-means (KM) and the Fuzzy C-means (FCM) algorithms were compared for their computing performance and clustering accuracy on different shaped cluster structures which are regularly and irregularly scattered in two dimensional space. While the accuracy of the KM with single pass was lower than those of the FCM, the KM with multiple starts showed nearly the same clustering accuracy with the FCM. Moreover the KM with multiple starts was extremely superior to the FCM in computing time in all datasets analyzed. Therefore, when well separated cluster structures spreading with regular patterns do exist in datasets the KM with multiple starts was recommended for cluster analysis because of its comparable accuracy and runtime performances.

Big data is technology that has the ability to manage very large amounts of data, in very fast time to allow real-time analysis and reactions. Several clustering methods which are used to group data are Fuzzy C-Means (FCM) and K-Means Clustering. K-Means Clustering algorithm is a method of partitioning existing data into two or more group. This research goal was to compare the performance of K-Means and Fuzzy C-Means algorithms in clustering data using big data technology. In this research, Hadoop and Hive were chosen the big data technology. The knowledge of Shia history on student and lecturer of Syarif Hidayatullah State Islamic University Jakarta were the data which used in this research, The testing was done by constructing application K-Means Fuzzy C-Means using Java language, Hadoop and Hive and then test the performance of K-Means and Fuzzy C-Means algorithms in data clustering. It compares both algorithms in terms of accuracy, execution time, and time complexity of the algorithms. In the application K-Means Fuzzy C-Means, evaluation were performed with data filter and the average accuracy difference result of K-Means and Fuzzy C-Means is 8.03% with the better accuracy owned by K-Means. The average execution time difference is 718.58 ms, which K-Means was faster than is Fuzzy C-Means. The time complexities of both algorithms have the same value O(n2) and the Big O equation resulted in an average difference of 93,568 with the smallest value on K-Means. Thus, K-Means algorithm is better than the Fuzzy C-Means in terms of accuracy, execution time, and the time complexity.

This work presents a detailed framework to detect the location of heart sound within the respiratory sound based on temporal fuzzy c-means (TFCM) algorithm. In the proposed method, respiratory sound is first divided into frames and for each frame, the logarithmic energy features are calculated. Then, these features are used to classify the respiratory sound as heart sound (HS containing lung sound) and non-HS (only lung sound) by the TFCM algorithm. The TFCM is the modified version fuzzy c-means (FCM) algorithm. While the FCM algorithm uses only the local information about the current frame, the TFCM algorithm uses the temporal information from both the current and the neighboring frames in decision making. To measure the detection performance of the proposed method, several experiments have been conducted on a database of 24 healthy subjects. The experimental results show that the average false-negative rate values are 0.8 ± 1.1 and 1.5 ± 1.4 %, and the normalized area under detection error curves are 0.0145 and 0.0269 for the TFCM method in the low and medium respiratory flow rates, respectively. These average values are significantly lower than those obtained by FCM algorithm and by the other compared methods in the literature, which demonstrates the efficiency of the proposed TFCM algorithm. On the other hand, the average elapsed time of the TFCM for a data with length of 0.2 ± 0.05 s is 0.2 ± 0.05 s, which is slightly higher than that of the FCM and lower than those of the other compared methods.

The tumor is a medical disorder that can be noticed in several parts of the body. A tumor in simple words is the abnormal growth of cells in a particular organ of the body without any control. These cells can interrupt the normal functioning of the brain. It can be found in people of any age, children, teenagers, and adults irrespective of gender. If the tumor is found in the brain, it is referred to as a brain tumor. Brain tumor must be analyzed accurately as it involves the life of a person. The tumor can be of Cancerous Tumor (CT) and Non-cancerous Tumor (NCT) types. The motive of the paper is to develop machine learning algorithms, which can detect the tumor without human interference and classify it as either CT or NCT. Machine learning algorithms developed are logistic regression and fuzzy c means methods. The logistic regression method is developed, it is a statistical model that helps in distinguishing categorical values. The sigmoid function is used to categorize the values. Furthermore, Fuzzy C-Means (FCM) method has also been developed. In the FCM method membership functions are calculated. Finding the similarity of data points is done and forming the clusters. The results of logistic regression and FCM are compared.

Cloud extraction and classification from satellite imagery is important for many applications in remote sensing. Satellite images are segmented based on distance, intensity and texture of the images. The popular segmentation algorithms, k‐means (KM) and fuzzy c‐means (FCM) clustering algorithms, face some problems such as unknown number of groups, unknown initialization and dead centers. In this paper, an unsupervised pixel classification by the KM and FCM algorithms is improved and the selection of centroids is made automatic. The proposed improved k‐means (IKM) and improved fuzzy c‐means (IFCM) clustering algorithms segment the INSAT‐3D satellite's thermal infrared image into low‐level, middle‐level, high‐level clouds and non‐cloudy region. As human beings can easily find the clouds in the satellite images, visible image is used to differentiate the clouds from the background. A threshold is found from the histogram of the visible image to separate the cloudy and non‐cloudy pixels. The other three thresholds to divide the clouds into three types are found from the thermal infrared image's histogram. The segmentation results of IKM and IFCM algorithms are compared with the existing segmentation algorithms. The comparison shows that IFCM algorithm matches well with original image followed by IKM algorithm as compared with existing algorithms.

Fuzzy clustering has been playing an important role in solving many problems. Fuzzy c-means (FCM) algorithm is most frequently used for fuzzy clustering. But some fixed points of FCM algorithm, known as Tucker's counter example, is not a reasonable solution. Moreover, the FCM algorithm is impossible to perform online learning since it is basically a batch learning scheme. This paper presents an unsupervised learning network as an attempt to improve the shortcomings of conventional clustering algorithms. This model integrates the optimization function of FCM algorithm into an unsupervised learning network. The learning rule of the proposed scheme is a result of formal derivation based on the gradient descent procedure of fuzzy objective function. Using the result of formal derivation, two implementations of the proposed scheme, the batch learning version and online learning version, are devised. They are tested on Chiou's data and Iris data and compared with FCM. Experimental results show that the proposed scheme derived the reasonable solution on Tucker's counter example.

In order to overcome the poor anti-noise performance of traditional fuzzy C-Means (FCM) algorithm in image segmentation, a novel improved FCM algorithm was proposed in this paper based on Particle Swarm Optimization (PSO) algorithm and Markov Random Field (MRF) model, which can make full use of the global searching ability of PSO and the spatial information integrating ability of MRF for image segmentation. In this algorithm, the image segmentation is converted to a PSO optimization problem, in which the fitness function is set up to containing the spatial information based on the spectral value and the neighboring pixels modeled by MRFs. And segmentation results can be iteratively obtained during the PSO iterations according to the newly designed membership function of FCM in which the spatial information is integrated. The experiments herein reported in this paper illustrate the better performance of this algorithm than the traditional FCM algorithm and the PSO algorithm for image segmentation.

Introduction:The segmentation method has a number of approaches, one of which is clustering. The clustering method is widely used for segmenting retinal blood vessels, especially the k-mean algorithm and fuzzy c-means (FCM). Unfortunately, so far there have been no studies comparing the two methods for blood vessel segmentation. Many studies do not explain the reason for choosing the method.Aim:This study aims to analyze the performance of the algorithms of k-means and FCM for retinal blood vessel segmentation.Methods:This research method is divided into three stages, namely preprocessing, segmentation, and performance analysis. Preprocessing uses the green channel method, Contrast-limited adaptive histogram equalization (CLAHE) and median filter. Segmentation is divided into three processes, namely clustering, thresholding and determining the region of interest (ROI). In the thresholding process, the determination of the threshold value uses two methods, namely the mean and the median. The third stage performs performance analysis using the performance parameters of the area under the curve (AUC) and statistical tests.Results:The statistical test results comparing FCM with k-means based on AUC values resulted in p-values <0.05 with a confidence level of 95%.Conclusion:Retinal vascular segmentation with the FCM method is significantly better than k-means.

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

Fuzzy C-Means (FCM) algorithm has good clustering efficiency, for which it is widely used in the field of image segmentation. However, problems such as weak robustness of distance measure, the number of initial clustering to be given in advance and not considering local image feature still exist. In essence, FCM is a local search algorithm. Improper selection of initial value will lead to the need for more iterations and convergence to local optimal solution. By combining evolving clustering (ECM) with FCM algorithm, a new method of remote sensing image segmentation is put forward. By using FCM algorithm to solve the choice of ECM's initialization clustering centers and using FCM to optimize the obtained centers, fuzzy clustering is completed. And by converting fuzzy into a certainty classification, clustering segmentation is realized. The algorithm is typical of relatively less iterations of convergence to global optimum, good stability and robustness. Experiment results show that it helps to produce better segmentation effect and improve the efficiency of remote sensing image segmentation.

Facies identification from basic well logs is essential for optimizing well operation or constructing reservoir model. The sequential nature of well log data makes the Hidden Markov Model (HMM) a suitable unsupervised machine learning approach for clustering. This study investigates the effectiveness of HMM combined with window filtering for unsupervised facies detection and compares its performance with the widely used K-Means Clustering (KMC) method. Synthetic gamma ray (GR), density (DEN), and neutron (NEU) logs, along with geologist-guided facies in a channel depositional environment, were used as input data. HMM identifies facies by modeling hidden geological states based on sequential well log data. Transition probabilities capture facies relationship across depths. A mode-based moving window filter smoothed the initial results by reducing fluctuations, while preserving facies boundaries. Validation was performed by comparing the results with reference facies using accuracy, adjusted rand index (ARI), normalized mutual information (NMI), and visual assessment. Both HMM and KMC clustering revealed thin non-geological facies, primarily at the facies boundaries. In the middle of facies, HMM mitigated thin facies more effectively than KMC. These thin facies arose from fluctuations in the well logs. Visually, HMM provided more geologically realistic results, despite KMC sometimes showed slightly better performance metrics. To address thin artificial facies, a moving window filter was applied. This slightly improved clustering performance metrics but significantly enhanced geological consistency by removing most artificial facies at boundaries or within facies, particularly in the smoothed HMM. Smoothed HMM achieved slightly better or comparable accuracy and NMI to smoothed KMC. However, minor thin facies persisted in both methods, more notably in KMC, due to significant well log fluctuations. The filter effectively smoothed the facies by reducing variations in thin facies across small depth intervals, ultimately enhancing the facies continuity. This improved facies distribution coherency with geological expectation, particularly for the smoothed HMM. HMM with post-processing plays a crucial role in identifying geologically consistent facies while maintaining or slightly improving clustering performance metrics. Unsupervised machine learning that incorporates sequential information is vital for facies clustering from well log data. The proposed workflow which integrates HMM with window filtering, enhances clustering outcomes by producing more geologically consistent result and more interpretable facies. This approach aids geoscientists in reservoir characterization and modeling, enabling more informed decision-making.

Asset integrity management of ageing oil and gas assets is an ongoing challenge. This paper uses unsupervised algorithms (i.e. clustering technique) to identify carbon steel piping with increased probability of failure due to various internal corrosion mechanisms. The application used over 20 variables including wellhead planktonic bacterial counts, Fe2+ levels, oil and water production rates, historical Non-Destructive Testing (NDT) records, remaining life of downstream equipment, previous remediation data and geographical location data. An unsupervised machine learning clustering algorithm was written grounded in mathematical techniques of Principal Component Analysis (PCA) and k-means clustering. The probabilistic algorithm identified implicit patterns, which were then used to identify critical and non-critical piping clusters. Outputs from the clustering model were used to prioritise field measurements, and while these are ongoing there appears to be a good agreement with model predictions. The paper further discusses the measures that have a higher impact on the classification accuracy of the algorithm.

Glioblastoma multiforme (GBM) are fast-growing brain tumours with grade IV malignancy. They have high proliferative activity and are thus considered lethal brain tumours. Hence, the diagnosis of brain GBM tumours in early stages could aid for better treatment. Computer-aided techniques are indispensable tools which can handle tumour segmentation with high speed and accuracy. In this paper, GBM segmentation was performed using k-means clustering (KM) and fuzzy c-means (FCM) clustering. The segmentation methods were evaluated and tested on T1 contrast-enhanced magnetic resonance image dataset of 78 patients. The performance of the two methods was compared using dice similarity coefficient (DSC). The average value of DSC for KM clustering and FCM clustering were 0.652 ± 0.105 and 0.64 ±0.121 respectively. The results obtained from this study demonstrates that KM clustering technique outperforms FCM clustering for GBM tumour segmentation.

Bone consists of an organic and an inorganic matrix. During development, bone undergoes changes in its composition and structure. In this study we apply three different cluster analysis algorithms [K-means (KM), fuzzy C-means (FCM) and hierarchical clustering (HCA)], and discriminant analysis (DA) on infrared spectroscopic data from developing cortical bone with the aim of comparing their ability to correctly classify the samples into different age groups. Cortical bone samples from the mid-diaphysis of the humerus of New Zealand white rabbits from three different maturation stages (newborn (NB), immature (11 days-1 month old), mature (3-6 months old)) were used. Three clusters were obtained by KM, FCM and HCA methods on different spectral regions (amide I, phosphate and carbonate). The newborn samples were well separated (71-100% correct classifications) from the other age groups by all bone components. The mature samples (3-6 months old) were well separated (100%) from those of other age groups by the carbonate spectral region, while by the phosphate and amide I regions some samples were assigned to another group (43-71% correct classifications). The greatest variance in the results for all algorithms was observed in the amide I region. In general, FCM clustering performed better than the other methods, and the overall error was lower. The discriminate analysis results showed that by combining the clustering results from all three spectral regions, the ability to predict the correct age group for all samples increased (from 29-86% to 77-91%). This study is the first to compare several clustering methods on infrared spectra of bone. Fuzzy C-means clustering performed best, and its ability to study the degree of memberships of samples to each cluster might be beneficial in future studies of medical diagnostics.

A good consumer profile can be developed by forming a suitable group of criteria. However, the most common existing consumer profile was created based on statistical averages where overlapping between characteristics does not exist. This paper discussed the potential of using the fuzzy clustering method to develop a suitable consumer profile. Fuzzy C-Means (FCM) algorithm has been selected to construct consumer profile from two different data sets; domestic water consumption and electricity consumption data sets. The result obtained using FCM algorithm was compared to the results produced by hard clustering algorithm and statistical method. The findings showed that fuzzy clustering has the potential to create a very informative consumer profile.