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An algorithm is proposed to solve the sensor subset selection problem. In this problem, a prespecified number of sensors are selected to estimate the value of a parameter such that a metric of estimation accuracy is maximized. The metric is defined as the determinant of the Bayesian Fisher information matrix (B-FIM). It is shown that the metric can be expanded as a homogenous polynomial of decision...
Constrained clustering through matrix factorization has been shown to largely improve clustering accuracy by incorporating prior knowledge into the factorization process. Although it has been well studied, none of them deal with constrained multi-way data factorization. Multi-way data or Tensors are encoded as high-order data structures. They can be seen as the generalization of matrices. One typical...
As the AHP is a relatively crude method of sorting, this paper is intended for improving the construction of AHP hierarchical structure model, which clusters the layer according to the grey cluster of the grey system, in order to reduce the inconsistency of judging matrix and the number of judging matrix. At the same time, the improvement can not only simplified the hierarchical structure, but also...
In this paper, an easily implemented semi-supervised graph learning method is presented for dimensionality reduction and clustering, using the most of prior knowledge from limited pairwise constraints. We extend instance-level constraints to space-level constraints to construct a more meaningful graph. By decomposing the (normalized) Laplacian matrix of this graph, to use the bottom eigenvectors leads...
Most of existing dimensionality reduction methods obtain the low-dimensional embedding via preserving a certain property of the data, such as locality, neighborhood relationship. However, the intrinsic cluster structure of data, which plays a key role in analyzing and utilizing the data, has been ignored by the state-of-the-art dimensionality reduction methods. Hence, in this paper we propose a novel...
This paper addresses one specific aspect of complexity reduction/interpretability improvement in fuzzy systems - how to limit the number of unique singletons in 0-th order Takagi-Sugeno (TS) systems, where the common practice is to assign an unique singleton to each rule. While abundance of free parameters makes 0-th order TS systems effective in data-driven identification, it also presents a computational...
Given a data matrix, the problem of finding dense/uniform sub-blocks in the matrix is becoming important in several applications. The problem is inherently combinatorial since the uniform sub-blocks may involve arbitrary subsets of rows and columns and may even be overlapping. While there are a few existing methods based on co-clustering or subspace clustering, they typically rely on local search...
We put forward a kind of spectral clustering for mixed data sets. The algorithm can process different kinds of attribute in mixed data sets, and then formed a comparability matrix. Based on the comparability matrix, we use the theory of spectral clustering to clustering the data sets. The experiment proves that this algorithm can cluster the data sets containing successive attribute and discrete attribute...
Document clustering usually involves high dimensional term space, which makes it difficult for organizing data into a small number of meaningful clusters. Clustering based on similar terms without considering the content or meaning is often unsatisfactory as it ignores the relationship between important terms that do not co-occur literally. In this paper, we propose to integrate the latent semantic...
The privacy-preserving recommendation system enables us to evaluate the recommended value without leaking the private information of users to service providers. The large overhead in performing cryptographical operations in proportion to the number of users and the number of items is the current issue. In this article, we propose some efficient schemes reducing the preference matrix of the sets of...
Privacy protection is an important issue in data processing. In this paper, we present a novel clustering method for privacy preserving in homogenous data sets. By developing matrix transformation method, our method can not only protect privacy in face of collusion, but also achieves a higher level of accuracy as compared to the existing method. The importance of independent perturbation is addressed...
The identification of transcription factor binding sites in promoter sequences is an important problem, since it reveals information about the transcription regulation of genes. In this paper, a novel motif discovery method based on motif clustering and matching is proposed. Against a precompiled library of motifs which is represented by position weight matrices(PWMs), each L-mer in the dataset is...
Clustering Web session is an important aspect of Web usage mining. In this paper, we propose a new algorithm of Web session fuzzy clustering, which applies the t-bridge algorithm to fuzzy equivalence matrix clustering algorithm. This algorithm is proved to have better accuracy, fewer CPU time and better scalability than others by the experiments.
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