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The FCM4DD fuzzy directional clustering algorithm, a simple, consistent and reliable method, requires the user to predefine the number of clusters. The determination of the number of clusters is very important in an unsupervised clustering algorithm. The number of clusters of directional data can be determined by observing scatter plots which are drawn in a one- or two-dimensional space. However,...
Consensus clustering, also known as clustering ensembles is a technique that combines multiple clustering solutions to obtain stable, accurate and novel results. Over the last years several consensus clustering approaches were proposed addressing practical clustering problems with different degrees of success. In this paper, we consider data fragments as elements of a cluster ensemble framework. We...
Real-world datasets consist of data representations (views) from different sources which often provide information complementary to each other. Multi-view learning algorithms aim at exploiting the complementary information present in different views for clustering and classification tasks. Several multi-view clustering methods that aim at partitioning objects into clusters based on multiple representations...
In this paper, we introduce an innovative fuzzy clustering model that includes some prior knowledge about the data. The prior knowledge is the data correlations expressed in a form of graph. Specifically, in this new model, we add a graph regularization term to the objective function of Fuzzy C-Mean (FCM) to fine-tune the final clustering result. By doing so, when we conduct fuzzy clustering to classify...
To solve the problem of weak semantic description ability of short text feature and feature sparse of short text, this paper proposes a user-orientedshort text clustering method. In the pre-process, this method uses semantic-independent word dictionary to identify and remove semantic-independent word, and then uses semantics class dictionary to normalize semantics. In addition, this paper proposes...
We bring out a runway extraction method based on rotating projection in this paper, which is consisted of three steps, locating the Region of interest (ROI), edge extraction and line detection. Firstly we employed template matching to locate the ROI which contains the runway area. Then we use Sobel operator to extract edges. The rotating projection algorithm is proposed to seek the potential straights...
Overlapping Clustering is an important technique in machine learning which aims to organize data into a set of non-disjoint groups rather than the disjoint one which is the case of conventional clustering methods. Several machine learning applications require that data object be assigned to one or several groups resulting in non-disjoint partitioning of data such as document clustering where each...
Recently, due to the huge growth of web pages, social media and modern applications, text clustering technique has emerged as a significant task to deal with a huge amount of text documents. Some web pages are easily browsed and tidily presented via applying the clustering technique in order to partition the documents into a subset of homogeneous clusters. In this paper, two novel text clustering...
The unsupervised analysis of data-sets, both large in dimension as well as in number of objects, are one of the most challenging tasks in data intense sciences. Especially in astronomy, dedicated survey telescopes generate an enormous amount of complex data. For example the database of the Sloan Digital Sky Survey (SDSS DR10) contains 3 million spectra with ca. 5,000 values each. Analyzing those spectra...
In this paper the problem of multi-scenario data driven fuzzy parameter estimation is considered. Experimental data are used from a small scale differentially steered four-wheel mobile robot “PROMETHEUS”. In particular two key modes of operation were identified and the multi-model parameters were obtained using the subtractive clustering approach. The two modes of the mobile robot operation were blended...
Fuzzy C-means algorithm (FCM) is a method of clustering which allows a point data to belong to two or more clusters. FCM algorithm suffers from outliers or noise because of the sum of membership values for an outlier point in all the clusters still being one. In this paper, an adapted FCM algorithm is proposed not only to detect the outliers but also remove the outliers to make FCM method robust....
In this paper, we present a novel semi-supervised clustering approach based on Markov process. It deals with data which include abundant local constraints. We apply the designed model to a topological region extraction problem, where topological segmentation is constructed based on sparse human inputs (potentially provided by human experts). The model considers human indications as seeds for topological...
Mixture models are frequently used to classify data. They are likelihood based models, and the maximum likelihood estimates of parameters are often obtained using the expectation maximization (EM) algorithm. However, multimodality of the likelihood surface means that poorly chosen starting points for optimisation may lead to only a local maximum, not a global maximum. In this paper, different methods...
Organizing wireless sensor networks into clusters enables the efficient utilization of the limited energy resources of the deployed sensor nodes. Oftentimes the network is organized into clusters of equal size, but such equal clustering results in an unequal load on the cluster head nodes. Instead, we propose an Unequal Clustering Method (UCM) for network organization, which can lead to more uniform...
Recently, semi-supervised clustering has been remarked and discussed in many research fields. In semi-supervised clustering, prior knowledge or information are often formulated as pairwise constraints, that is, must-link and cannot-link. Such pairwise constraints are frequently used in order to improve clustering properties. In this paper, we will propose a new semi-supervised fuzzy c-means clustering...
In this paper, a document clustering method that use the weighted semantic features and cluster similarity is introduced to cluster meaningful topics from document set. The proposed method can improve the quality of document clustering because it can avoid clustering the documents whose similarities with topics are high but are meaningless between cluster and document by using the weighted semantic...
Takagi-Sugeno models are an important class of fuzzy rule based oriented models, generally used for prediction and control. Fuzzy clustering is one of effective methods for identification. In this method, we propose to use a fuzzy clustering method (Kernel based fuzzy c-means method) for automatically constructing a multi-input fuzzy model to identify the structure of a fuzzy model. To clarify the...
This paper investigates the cluster problems of which both the characteristic values and weights of indices are triangular fuzzy numbers. A new maximal tree clustering analysis method is proposed. First, several operational laws of two triangular fuzzy numbers are given. Next, the similarity coefficient is generalized to the triangular fuzzy numbers. Then an clustering algorithm for multiple attribute...
In this paper, an objective function based approach is presented to characterize a fuzzy classifier system via a kernel learning algorithms for non-linear data. We combine the distance based kernel fuzzy clustering and the non-linear support vector classification (SVC) with a conjoint objective based fuzzy clustering method in a novel way in order to learn a fuzzy classifier system. The two objectives...
This paper proposes a fuzzy clustering method under the intrinsically classified structure of data through dissimilarity of objects at each variable. In order to extract the classification structure, the variable-based fuzzy clustering method is exploited and the degree of classification for each object with respect to each variable is defined. This degree shows individually classified power of an...
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