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The bag of words (BOW) represents a corpus in a matrix whose elements are the frequency of words. However, each row in the matrix is a very high-dimensional sparse vector. Dimension reduction (DR) is a popular method to address sparsity and high-dimensionality issues. Among different strategies to develop DR method, Unsupervised Feature Transformation (UFT) is a popular strategy to map all words on...
Greenhouse climate is difficult to model as a complex nonlinear system. The solution to the problem of describing the relation between inputs and outputs is using T-S fuzzy modeling which could transform a nonlinear system into several linear systems. During the transition, c-means clustering is used to cluster the variable of inputs and outputs. The result of clustering determines the compose of...
In many applications such as music transcription, audio forensics, and speech source separation, it is needed to decompose a mono recording into its respective sources. These techniques are usually referred to as blind source separation (BSS). One of the methods recently used in BSS is non-negative matrix factorization (NMF) both in supervised and unsupervised learning cases. In this paper, we propose...
The random Fourier Features method has been found very effective in approximating the kernel functions. Our former studies show that through a mixing mechanism of the feature space formed by random Fourier features and certain linear algorithms, the fuzzy clustering results in the approximated feature space are comparable to or even exceed the classical kernel-based algorithms. To increase the robustness...
In semi administered bunching is one of the vital errands and goes for gathering the information objects into classes (groups) to such an extent that the similitude of items inside bunches is high and the comparability of articles between bunches is Less. The dataset once in a while might be in blended nature that is it might comprise of both numeric and unmitigated sort of information. So two types...
Testing of software is a worthwhile aspect of software development life cycle. Effective and efficient test cases must be designed to test the software which will reduce the testing cost, time and effort. Nowadays, testing an aspect-oriented program is becoming a challenge for the testers. This paper proposes a novel approach to generate test case scenarios for an aspect oriented program derived from...
Unsupervised fuzzy clustering is an important tool for finding the meaningful patterns in data sets. In fuzzy clustering analyses, the performances of clustering algorithms are mostly compared using several internal fuzzy validity indices. However, since the well-known fuzzy indices have originally been proposed for working with membership degrees produced by the traditional Fuzzy c-means Clustering...
Bearings are one of the most omnipresent and vulnerable components in rotary machinery such as motors, generators, gearboxes, or wind turbines. The consequences of a bearing fault range from production losses to critical safety issues. To mitigate these consequences condition based maintenance is gaining momentum. This is based on a variety of fault diagnosis techniques where fuzzy clustering plays...
The collaborative approach has shown interest in several fields of application, particularly in information retrieval to satisfy a need for shared information. Despite this collaboration, the search for relevant information is always a tedious task as long as the mass of information continues to increase, part of which is a source, while other parties represent comments on these sources. It is obvious...
As one of the most popular methods for image segmentation, fuzzy C-means algorithm suffers two unavoidable initialization difficulties including obtaining initial cluster centroids and deciding cluster number, which affect the algorithm performance. Motivated by the above, an automatic fuzzy clustering algorithm is proposed in this paper, where observation matrix, judgment matrix and set partitioning...
A new methodology to improve the performance of real-time open switch fault isolation scheme in multilevel multiphase converters has been proposed in this paper. Within the presented framework, the fault is detected by a model-based method using self-recurrent wavelet neural network. Afterward, a semi-supervised fuzzy clustering technique is applied to locate the failed switch while multiresolution...
Particle Swarm Optimization (PSO) based Fuzzy c-means (FCM) methods typically use random initialization, and could incur substantial computation costs in processing big data, although PSO facilitates the global optimization, based on our previous work [1]. This paper further developed and evaluated our data density-pattern based algorithm to guide initialization and to achieve better computational...
Extracting intuitive and useful information from the high-dimensional, fuzzy and complex operational simulation training data is in urgent need. In this paper, the operational simulation training data refers to the quantitative, numeric data that is usually used as the simulation results. The traditional statistical analysis methods have some limitations in clustering, visualizing and evaluating the...
Clustering accuracy of fuzzy clustering is sensitive to the structure of dataset to be studied. Semi-supervised clustering algorithms aim to increase the accuracy under the supervisions of a limited amount of labeled data, but the classification rate is highly dependent on the size of available labeled data. To overcome this disadvantage, we propose a novel semi-supervised clustering based on label...
The collaborative information in horizontal collaborative fuzzy clustering is transmitted by partition matrix, which requires that the dimensions of collaborating partition matrix and collaborated partition matrix must be the same. It requires that the collaborative datasets are clustered into the same number of clusters, but in many cases it is not suitable or difficult to do. In this paper, a new...
Multicolour fluorescence in-situ hybridization (M-FISH) images can be used to detect chromosomal abnormalities, which subsequently can be used for the diagnosis of certain cancers and genetic diseases. However, there is currently no automated system able to achieve a sufficient accuracy for clinical purposes. The accuracy of segmentation and classification of pixels within these images is highly important...
The study deals with the analyzing of the technique for identification of fuzzy models which are using in order to assess electrical equipment condition with uncertainty of input data. The research method is based on using fuzzy cluster analysis algorithms for constructing the membership functions and determining the effectiveness of these algorithms according to the criterion of fuzzy models accuracy...
This paper proposes a score based computational technique for the detection of non-technical losses in electricity distribution networks. The methodology is comprised of three steps. In the first one, a score is assigned to each meter number considering the area that customers live. In second step, a C-means-based fuzzy clustering is applied to find consumers with similar consumption profiles. Then,...
A variety of fuzzy clustering methods exploit the Euclidean distance to quantify resemblance between data points. This distance is effective for revealing spherical clusters, but it does not perform well for data exhibiting more complicated geometry. So, in this paper, we present a new algorithm IFPCM with Minkowski distance applied on real and artificial datasets being generated according to various...
Distributed Applications from different domains like Health care, E-Commerce, science, social networks etc., tend to generate large volumes of heterogeneous data that grow exponentially over a period of time leading to big data sets. Descriptive Analytics, on big data sets, pose a great challenge for traditional data analytical tools, since it is to be performed on the full data set, unlike predictive...
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