Fuzzy Maximum Likelihood Estimation Research Articles

Optimized Fuzzy Cmeans – Fuzzy Covariance – Fuzzy Maximum Likelihood Estimation Clustering Method Based on Deferential Evolutionary Optimization Algorithm for Identification of Rock Mass Discontinuities Sets

Detecting of joint sets (clusters) is one of the most important processes in determining properties of fractures. Joints clustering and consequently, determination of the mean value representing each cluster is applicable to most rock mass studies. It is clear that the accuracy of the clustering process plays a key role in analyzing stability of infrastructures such as dams and tunnels and so on. Hence, in this paper, by reviewing several methods proposed for clustering fractures and considering their advantages and disadvantages, a three-stage hybrid method is developed which contains Fuzzy c-means, Fuzzy covariance and Fuzzy maximum likelihood estimation that by utilizing the modified orientation matrix had been optimized. This method is optimized by the Differential Evolutionary algorithm using a new and strong cost function which is defined as the computation core. In addition, using three clustering quality comparing criteria, the new developed method of differential evolutionary optimized of fuzzy cmeans - fuzzy covariance - fuzzy maximum likelihood estimation clustering method (DEF3) is compared with other base and common methods using field data. After doing the calculations, the developed method by giving the best values for all the criteria provided the best results and good stability in meeting different criteria. The DEF3 method was validated using actual field data which mapped in Rudbar Lorestan dam site. The results revealed that DEF3 acquired the best rank among the other method by getting the value of 0.5721 of Davis-Bouldin criterion, 1403.1 of Calinski-Harabasz criterion, and 0.83482 of Silihotte as comparing criteria of clustering methods.

On first quartile journals which are not of highest impact

Here we study the relationship between journal quartile rankings of ISI impact factor (at the 2010) and journal classification in four impact classes, i.e., highest impact, medium highest impact, medium lowest impact, and lowest impact journals in subject category computer science artificial intelligence. To this aim, we use fuzzy maximum likelihood estimation clustering in order to identify groups of journals sharing similar characteristics in a multivariate indicator space. The seven variables used in this analysis are: (1) Scimago Journal Ranking (SJR); (2) H-Index (H); (3) ISI impact factor (IF); (4) 5-Year Impact Factor (5IF); (5) Immediacy Index (II); (6) Eigenfactor Score (ES); and (7) Article Influence Score (AIS). The fuzzy clustering allows impact classes to overlap, thereby accommodating for uncertainty related to the confusion about the impact class attribution for a journal and vagueness in impact classes definition. This paper demonstrates the complex relationship between quartiles of ISI impact factor and journal impact classes in the multivariate indicator space. And that several indicators should be used for a distinct analysis of structural changes at the score distribution of journals in a subject category. Here we propose it can be performed in a multivariate indicator space using a fuzzy classifier.

POLARIMETRIC-SAR CLASSIFICATION USING FUZZY MAXIMUM LIKEHOOD ESTIMATION CLUSTERING WITH CONSIDERATION OF COMPLEMENTARY INFORMATION BASED ON PHYSICAL POLARIMETRIC PARAMETERS, TARGET SCATTERING CHARACTERISTIK, AND SPATIAL CONTEXT

This paper shows a study on an alternative method for unsupervised classification of polarimetric-Syenthetic Aperture Radar (SAR) data. The first step was to extract several main physical polarimetric parameters (polarization power, coherence, and phase difference) from polarimetric covariance matrix (or coherency matrix) and physical scattering characteristics of land use/cover based on polarimetric decomposition (Cloude decomposition model). In this paper, we found that these features have complementary information which can be integrated in order to improve the discrimination of different land use or cover types. Classification stage was performed using Fuzzy Maximum Likelihood Estimation (FMLE) clustering algorithm. FMLE algorithm allows for ellipsoidal clusters of arbitrary extent and is consequently more flexible than standard Fuzzy K-Means clustering algorithm. Hoever, basic FMLE algorithm makes use exclusively the spectral (or intensity) properties of the individual pixel vectors and spatial-contextual information of the image was not taken into account. Hence, poor(noisy) classification result is ussualy obtained from SAR data due to speckle noise. In this paper, we propose a modified FMLE which integrate basic FMLE clustering with spatial-contextual information by statistical analysis of local neightbourhoods. The effectiveness of the proposed method was demonstrated using E-SAR polarimetric data acquired on the area of Penajam, East Kalimantan, Indonesia. Result showed classified images improving land-cover discrimination performance. Exhibiting homogeneous region, and preserving edge and other fine structures. Keywords: Cloudes polarimetric decomposition, FMLE clustering, polarimetric coherence, Polarimetric-SAR, unsupervised classification.

Fuzzy clustering to detect tuberculous meningitis-associated hyperdensity in CT images

Hyperdensity in head CT images has been shown to be a specific feature for diagnosing tuberculous meningitis (TBM) in children. We describe the extraction of hyperdense regions using fuzzy c-means clustering and fuzzy maximum likelihood estimation, thus providing a tool for the enhancement of an often subtle radiological feature. We calculate an asymmetry measure and confirm that normal and TBM images have different patterns of hyperdensity. Our results may be used in computer-assisted diagnosis of TBM.

Data analysis with fuzzy clustering methods

An encompassing, self-contained introduction to the foundations of the broad field of fuzzy clustering is presented. The fuzzy cluster partitions are introduced with special emphasis on the interpretation of the two most encountered types of gradual cluster assignments: the fuzzy and the possibilistic membership degrees. A systematic overview of present fuzzy clustering methods is provided, highlighting the underlying ideas of the different approaches. The class of objective function-based methods, the family of alternating cluster estimation algorithms, and the fuzzy maximum likelihood estimation scheme are discussed. The latter is a fuzzy relative of the well-known expectation maximization algorithm and it is compared to its counterpart in statistical clustering. Related issues are considered, concluding with references to selected developments in the area.

AN EXTENDED FUZZY CLUSTERING ALGORITHM AND ITS APPLICATION

For data analyses, it is very important to combine data with similar attribute values into a categorically homogeneous subset, called a cluster, and this technique is called clustering. Generally crisp clustering algorithms are weak in noise, because each datum should be assigned to exactly one cluster. In order to solve the problem, a fuzzy c-means, a fuzzy maximum likelihood estimation, and an optimal fuzzy clustering algorithms in the fuzzy set theory have been proposed. They, however, require a lot of processing time because of exhaustive iteration with an amount of data and their memberships. Especially large memory space results in the degradation of performance in real-time processing applications, because it takes too much time to swap between the main memory and the secondary memory. To overcome these limitations, an extended fuzzy clustering algorithm based on an unsupervised optimal fuzzy clustering algorithm is proposed in this paper. This algorithm assigns a weight factor to each distinct datum considering its occurrence rate. Also, the proposed extended fuzzy clustering algorithm considers the degree of importances of each attribute, which determines the characteristics of the data. The worst case is that the whole data has an uniformly normal distribution, which means the importance of all attributes are the same. The proposed extended fuzzy clustering algorithm has better performance than the unsupervised optimal fuzzy clustering algorithm in terms of memory space and execution time in most cases. For simulation the proposed algorithm is applied to color image segmentation. Also automatic target detection and multipeak detection are considered as applications. These schemes can be applied to any other fuzzy clustering algorithms.

Unsupervised optimal fuzzy clustering

This study reports on a method for carrying out fuzzy classification without a priori assumptions on the number of clusters in the data set. Assessment of cluster validity is based on performance measures using hypervolume and density criteria. An algorithm is derived from a combination of the fuzzy K-means algorithm and fuzzy maximum-likelihood estimation. The unsupervised fuzzy partition-optimal number of classes algorithm performs well in situations of large variability of cluster shapes, densities, and number of data points in each cluster. The algorithm was tested on different classes of simulated data, and on a real data set derived from sleep EEG signal. >