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Kernel partial least squares(KPLS) is widely adopted for soft-sensing in nonlinear industrial process. For KPLS method, the determination of central nodes and kernel width in the kernel function will affects generalization ability and predictiability. This paper proposes an entropy-clustering and K-means based KPLS regression method. First of all, it divides the original data into several clusters...
C-means had been used for data clustering problems for recently years. However, if it uses the non-robust objective function of FCM (Fuzzy C-Means), we will get poor result if data corrupted because some noises. To improve these problems, this paper make effective objective functions of Fuzzy C-means which named MVDFCM (Mean Variable Distance Fuzzy C-means). The method is with center learning method...
In this paper, we present a relaxed version of the SpecHybrid Algorithm originally proposed for wireless cellular systems, and apply it to text document clustering problem. We conduct several experiments on two different datasets; a widely used benchmark dataset in English, and a Turkish textual dataset commonly used in text classification. Our results show that the proposed algorithm gives superior...
Arabic Documents Clustering is an important task for obtaining good results with the traditional Information Retrieval (TR) systems especially with the rapid growth of the number of online documents present in Arabic language. Document clustering aims to automatically group similar documents in one cluster using different similarity/distance measures. In this paper, we evaluate the impact of the stemming...
Document clustering is related to data clustering concept which is one of data mining tasks and unsupervised classification. It is often applied to the huge data in order to make a partition based on their similarity. Initially, it used for Information Retrieval in order to improve the precision and recall from query. It is very easy to cluster with small data attributes which contains of important...
The biclustering method is a very useful tool for analyzing gene expression data when some genes have multiple functions and experimental conditions are diverse in gene expression measurement. It focuses on finding a subset of genes and a subset of experimental conditions that together exhibit coherent behavior. A large number of biclustering algorithms has been developed for analyzing gene expression...
We generalize the notions of centroids and barycenters to the broad class of information-theoretic distortion measures called Bregman divergences. Because Bregman divergences are typically asymmetric, we consider both the left-sided and right-sided centroids and the symmetrized centroids, and prove that all three are unique. We give closed-form solutions for the sided centroids that are generalized...
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