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PCA-guided k-Means performs non-hierarchical hard clustering based on PCA-guided subspace learning mechanism in a batch process. Sequential Fuzzy Cluster Extraction (SFCE) is a procedure for analytically extracting fuzzy clusters one by one, and is useful for ignoring noise samples. This paper considers a hybrid concept of the two clustering approaches and proposes a new robust k-Means algorithm that...
This paper proposes a hierarchical architecture, HieNet, that utilizes the K-Iterations Fast Learning artificial Neural Network (KFLANN). Effective in its clustering capabilities, the KFLANN is capable of providing more stable and consistent clusters that are independent data presentation sequences (DPS). Leveraging on the ability to provide more consistent clusters, the KFLANN is initially used to...
In this paper, an improved clustering algorithm based Ant-Tree is used for recognition of certain kind of architectural symbols with prior knowledge in engineering drawings. Symbols are segmented from an AutoCAD format drawing and a vector of invariants based on pseudo-Zernike moments is calculated to represent the graphical feature of a symbol. A normalization method is used to make these moments...
Due to the rapid development of motion capture technology, more and more human motion databases appear. In order to effectively and efficiently manage human motion database, human motion classification is necessary. In this paper, we propose an ensemble based human motion classification approach (EHMCA). Specifically, EHMCA first extracts the descriptors from human motion sequences. Then, singular...
In this paper, we propose a new algorithm called fuzzy cluster ensemble algorithm (FCEA) which integrates the fuzzy logic theory and traditional cluster ensembles for 3D head model classification. Specifically, FCEA consists of two parts: (i) data processing on the distributed locations and (ii) data fusion on the centralized location. In the distributed locations, data processing includes (i) extracting...
Pattern mining gains more and more attention due to its useful applications in many areas, such as machine learning, database, multimedia, biology, and so on. Though there exist a lot of approaches for pattern mining, few of them consider the local distribution of the data. In the paper, we not only design six challenge datasets related to the local patterns, but also propose a new pattern mining...
Although there exist a lot of cluster ensemble approaches, few of them consider the prior knowledge of the datasets. In this paper, we propose a new cluster ensemble approach called knowledge based cluster ensemble (KCE) which incorporates the prior knowledge of the dataset into the cluster ensemble framework. Specifically, the prior knowledge of the dataset is first represented by the side information...
The algorithm of least square support vector machine (LSSVM) based on fuzzy c-means (FCM) clustering is presented in this paper, which can select the number of clusters automatically depending on different parameters and samples. We adopt the method to identify the inverse system with crucial spanless process variables and the inenarrable nonlinear character. In the course of identification, we construct...
We present an analytic and geometric view of the sample mean of graphs. The theoretical framework yields efficient subgradient methods for approximating a structural mean and a simple plug-in mechanism to extend existing central clustering algorithms to graphs. Experiments in clustering protein structures show the benefits of the proposed theory.
The study of the neuronal correlates of the spontaneous alternation in perception elicited by bistable visual stimuli is promising for understanding the mechanism of neural information processing and the neural basis of visual perception and perceptual decision-making. In this paper we apply a sparse nonnegative tensor factorization (NTF) based method to extract features from the local field potential...
Clustering with constraints is an active area in machine learning and data mining. In this paper, a semi-supervised kernel-based fuzzy C-means algorithm called PCKFCM is proposed which incorporates both semi-supervised learning technique and the kernel method into traditional fuzzy clustering algorithm. The clustering is achieved by minimizing a carefully designed objective function. A kernel-based...
Determining the optimum number of clusters is an ill posed problem for which there is no simple way of knowing that number without a priori knowledge. The purpose of this paper is to provide a simultaneous two-level clustering algorithm based on self organizing map, called DS2L-SOM, which learn at the same time the structure of the data and its segmentation. The algorithm is based both on distance...
Semantic scene classification, robotic state recognition, and many other real-world applications involve multi-label classification with imbalanced data. In this paper, we address these problems by using an enrichment process in neural net training. The enrichment process can manage the imbalanced data and train the neural net with high classification accuracy. Experimental results on a robotic arm...
Nonnegative matrix factorization (NMF) is a widely-used method for multivariate analysis of nonnegative data, the goal of which is decompose a data matrix into a basis matrix and an encoding variable matrix with all of these matrices allowed to have only nonnegative elements. In this paper we present simple algorithms for orthogonal NMF, where orthogonality constraints are imposed on basis matrix...
Time series clustering finds applications in diverse fields of science and technology. Kernel based clustering methods like kernel K-means method need number of clusters as input and cannot handle outliers or noise. In this paper, we propose a density based clustering method in kernel feature space for clustering multivariate time series data of varying length. This method can also be used for clustering...
Software metrics are collected at various phases of the software development process. These metrics contain the information of the software and can be used to predict software quality in the early stage of software life cycle. Intelligent computing techniques such as data mining can be applied in the study of software quality by analyzing software metrics. Clustering analysis, which can be considered...
The clustering problem consists in the discovery of interesting groups in a dataset. Such task is very important and widely tackled in the literature. In this paper, we propose an evolutionary method in order to obtain well formed and spatially separated clusters. The proposed algorithm uses a complete solution representation, each partition is represented by a length-variable chromosome. The variation...
We review a form of topology preserving mapping which uses the same underlying structure as the generative topographic mapping (GTM) but organises the projections of the latent points into data space based on the method of harmonic K-means. We show that projections of the Olivetti face database onto this latent space show good performance in terms of identifying all images of a particular individual...
High dimensionality, noisy features and outliers can cause problems in cluster analysis. Many existing methods can handle one of the problems well but not the others. In this paper, we propose a new clustering algorithm to solve these problems. The basic idea is to control the support of the optimization procedure so that the effect produced by those contaminated samples and dimensions is greatly...
Kernel k-means is an extension of the standard k-means clustering algorithm that identifies nonlinearly separable clusters. In order to overcome the cluster initialization problem associated with this method, in this work we propose the global kernel k-means algorithm, a deterministic and incremental approach to kernel-based clustering. Our method adds one cluster at each stage through a global search...
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