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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 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...
Many companies spend vast amounts of resources to collect, transform and store the massive amounts of data that flows through their business processes. When it comes to doing analysis and machine learning such as clustering on this data, time and compute speed gate determine how much data can be analyzed. Moreover, most Big Data clustering algorithms do not look at a complete, large dataset. Instead,...
A powerful and flexible organization of documents can be obtained by mixing fuzzy and possibilistic clustering. In such organization, documents can belong to more than one cluster simultaneously with different compatibility degrees. Clusters represent topics, which are identified by one or more descriptors extracted by a proposed method. In this manuscript, we investigated whether or not the descriptors...
In this paper an idea for classification of energy consumption profiles using an evolving c-regression method is presented. Cluster prototypes (centers) are usually defined as a mean of data around the center. The cluster center is a vector of numerical values. The method presented in this paper uses Takagi-Sugeno fuzzy models as a cluster prototype. Beside the method description also preliminary...
Segmentation for images with intensity inhomogeneity is very difficult. In this paper, a fuzzy clustering-based method to segment intensity inhomogeneity images is presented. Firstly, a new expression of the fuzzy C-means(FCM) object function is derived through altering the prototype of every clustering to a point-wise function. Then, a weight function defined on the local window is introduced into...
With the rapid development of information society, intricate relationship between objects establish huge heterogeneous networks. The linkage is affected by multiple factors, which makes community detection on heterogeneous network a difficult task. Traditional clustering algorithms focus on divided factors, ignoring the combination of them. If the structure of multi-dimensional information is taken...
There have been numerous studies on using the FCM algorithm in clustering and collaboration clustering, especially in data analysis, data mining and pattern recognition. In this study, we present new methods involving interval Type-2 fuzzy sets to realize collaborative clustering. Data in which the clustering results realized at one data site impact clustering carried out at other data sites. Those...
Most datasets obtained in real-world applications are typically unlabeled, requiring a manual labor of classifying a sample of such data or the application of unsupervised learning. Clustering is typically used to devise how data are grouped together before sampling the data to be labeled. Most clustering algorithms often assumes that the number of clusters is known and that a given instance from...
System flexibility means the ability of a system to manage imprecise and/or uncertain information. A lot of commercially available Information Retrieval Systems (IRS) address this issue at the level of query formulation. Another way to make the flexibility of an IRS possible is by means of the flexible organization of documents. Such organization can be carried out using clustering algorithms by which...
With consensus tendency, fuzzy clustering results are given in advance and the goal is to reconcile the results by building aggregate partition and prototypes. In this paper, we present a consensus-driven fuzzy clustering algorithm in which distributed data are progressively merged into intersites' clusters through global aggregation based on similarity relationships. The ensuring algorithm considers...
Datasets with missing values are frequent in clustering analysis. It seems obvious that the reconstruction of missing attribute values can be considered as the key factors impacting the clustering performance. For this, a FCM clustering algorithm for incomplete data sets based on human-computer cooperation is proposed in this paper. On account of the uncertainty of missing attributes, intervals are...
Soft subspace fuzzy clustering algorithms have been successfully utilized for high dimensional data in recent studies. However, the existing works often utilize only one distance function to evaluate the similarity between data items along with each feature, which leads to performance degradation for some complex data sets. In this work, a novel soft subspace fuzzy clustering algorithm MKEWFC-K is...
In this paper a new approach to data stream evolving fuzzy model identification is given. The structure of the model is given in the form of Takagi-Sugeno and the partitioning of the input-output space is obtained using a fuzzy c-regression clustering method and the approach also involves the evolving properties. The method is given in a recursive form. The proposed approach is shown with two simple...
This paper presents two incremental clustering algorithms based on FCMK, a fuzzy clustering with multiple kernels algorithm we developed earlier [1]. The FCMK algorithm has a memory requirement of O(N2), where N is the number of objects in the data set. Thus, even data sets that have nearly 1, 000, 000 objects require terabytes of working memory-impractical for most computers. One way to attack this...
Fuzzy clustering has been one of the commonly used vehicles to construct information granules (whose description is provided in terms of prototypes and partition matrices). The quality of resulting information granules can be assessed by quantifying how well the original numeric data from which information granules have been constructed can be represented (granulated) by information granules and subsequently...
This paper presents partitioning fuzzy clustering algorithms for mixed feature-type symbolic data. The proposed algorithms need a previous pre-processing step in order to obtain a suitable homogenization of the mixed feature-type symbolic data into histogram-valued symbolic data. These fuzzy clustering algorithms give a fuzzy partition and a prototype for each fuzzy cluster by optimizing an adequacy...
This paper presents partitioning fuzzy clustering algorithms for interval-valued data. These fuzzy clustering algorithms give a fuzzy partition and a prototype for each fuzzy cluster by optimizing an adequacy criterion based on suitable adaptive and non-adaptive Hausdorff distances between vectors of intervals. The adaptive Hausdorff distances change at each algorithm iteration and are different from...
While classical kernel-based clustering algorithms are based on a single kernel, in practice it is often desirable to base clustering on combination of multiple kernels. In [1], we considered a fuzzy c-means with multiple kernels in observation space (FCMK-OS) algorithm which constructs the kernel from a number of Gaussian kernels and learns a resolution specific weight for each kernel function in...
Data clustering is useful in several areas, such as web mining, biology, climate, medical diagnosis, computer vision, marketing and others. Thus, in real problems, data can simultaneously belong to more than one cluster, being necessary to use fuzzy clustering concepts as decision mechanisms to assign data into clusters. Moreover, nature-based intelligent mechanisms have been used to increase the...
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