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The work carried out here, introduces an improved mixed method for order reduction of interval systems based on Fuzzy C-Means (FCM) clustering algorithm along with Pade Approximation method. In proposed technique for a given interval system the reduced order denominator is constructed through FCM clustering using dominant pole approach. The added impact of these two approaches tries to capture the...
The use of map based representative velocity profiles allows to predict the future state of a vehicle. The suggested approach is based on Fast Dynamic Time Warping. Spectral clustering is used to distinguish velocity profiles. Applying abstraction can significantly reduce computation time with a minor effect on cluster allocation. Outlier removal increases the quality of cluster identification. The...
Automatic text summarization has become a relevant topic due to the information overload. This automatization aims to help humans and machines to deal with the vast amount of text data (structured and un-structured) offered on the web and deep web. In this paper a novel approach for automatic extractive text summarization called SENCLUS is presented. Using a genetic clustering algorithm, SENCLUS clusters...
Exact methods for Agglomerative Hierarchical Clustering (AHC) with average linkage do not scale well when the number of items to be clustered is large. The best known algorithms are characterized by quadratic complexity. This is a generally accepted fact and cannot be improved without using specifics of certain metric spaces. Twister tries is an algorithm that produces a dendrogram (i.e., Outcome...
This work introduces the use of co-clustering for hyperspectral image analysis. Co-clustering is able to simultaneously group samples (rows) and spectral bands (columns). This results in blocks, which do not only share spectral information (classical one way clustering) but also share sample information. Here, we propose using a co-clustering algorithm based on Information Theory — the optimal co-clustering...
Depending on the nature of CSP instances to consider, the decomposition methods offer an approach often efficient for the solving, the counting of solutions or the optimization. So, the community has focused a large part of its efforts on the design of algorithms aiming to find the best decompositions, i.e. ones which minimize the width of the decomposition, the fundamental parameter in terms of theoretical...
Many social networks and complex systems are found to be naturally divided into clusters of densely connected nodes, known as community structure (CS). Finding CS is one of fundamental yet challenging topics in network science. One of the most popular classes of methods for this problem is to maximize Newman's modularity. However, there is a littleunderstood on how well we can approximate the maximum...
Clustering is a task of finding natural groups in datasets based on measured or perceived similarity between data points. Spectral clustering is a well-known graph-theoretic approach, which is capable of capturing non-convex geometries of datasets. However, it generally becomes infeasible for analyzing large datasets due to relatively high time and space complexity. In this paper, we propose Multi-level...
The exponential growth of the size of the search space has always been an obstacle to POMDP planning. Heuristics are often used to reduce the search space size and improve computational efficiency. As the advantage of the feature of POMDP problems should be taken into deeper consideration, we analyze the clustering feature of reachable space of POMDP problems and apply policy iteration based on this...
Previous research has aimed to lower the cost of handling large networks by reducing the network size via sparsification. However, when many edges are removed from the network, the information that can be used for link prediction becomes rather limited, and the prediction accuracy thereby drops significantly. To address this issue, we propose a framework called Diverse Ensemble of Drastic Sparsification...
Clustering items using textual features is an important problem with many applications, such as root-cause analysis of spam campaigns, as well as identifying common topics in social media. Due to the sheer size of such data, algorithmic scalability becomes a major concern. In this work, we present our approach for text clustering that builds an approximate k-NN graph, which is then used to compute...
We give a 2{n+o(n)}-time and space randomized algorithm for solving the exact Closest Vector Problem (CVP) on n-dimensional Euclidean lattices. This improves on the previous fastest algorithm, the deterministic ~{O}(4n)-time and ~{O}(2n)-space algorithm of Micciancio and Voulgaris. We achieve our main result in three steps. First, we show how to modify the sampling algorithm...
Much of the data of scientific interest, particularly when independence of data is not assumed, can be represented in the form of networks where data nodes are joined together to form edges corresponding to some kind of associations or relationships. Such information networks abound, like protein interactions in biology, web page hyperlink connections in information retrieval on the Web, cellphone...
This paper addresses the (r, 1 + e)-approximate near neighbor problem ( or (r, 1 + e)-NN) that is defined as follows: given a set of n points in a d-dimensional space, a query point q and parameter 0 < d < 1, build a data structure that reports a point within distance (1 + e)r from q with probability 1 -- d, if there is a point in the data set within distance r from q. We present an algorithm...
Network theory has progressed a long way since the Erdős-Rényi model, identifying many important real-world phenomena that a good random graph model should capture, and producing more realistic models to capture many of them. However, these models are largely limited to the domain of simple networks — nodes and links only — leaving remaining complications outside the realm of theory. In such cases,...
Feature selection for clustering is a challenging problem due to the absence of class labels. Existing approaches can select a feature subset to maintain clustering performance while reducing dimensionality. However, we are faced with two problems: (1) there could be many sets of features that seem equally good, and (2) these features are sensitive to small data perturbation, or the selection instability...
Recently, many sparse approximation methods have been applied to solve spectral unmixing problems. These methods in contrast to traditional methods for spectral unmixing are designed to work with large a-prori given spectral dictionaries containing hundreds of labelled material spectra enabling to skip the expensive endmember extraction and labelling step. However, it has been shown that sparse approximation...
Cloud computing, featured by shared servers and location independent services, has been widely adopted by various businesses to increase computing efficiency, and reduce operational costs. Despite significant benefits and interests, enterprises have a hard time to decide whether or not to migrate thousands of servers into the cloud because of various reasons such as lack of holistic migration (planning)...
Approximate spectral clustering (ASC), a recently popular approach for unsupervised land cover identification, applies spectral clustering on a reduced set of data representatives (found by sampling or quantization). ASC enables extraction of clusters with different characteristics by utilizing various information types (such as distance, local density distribution and data topology) for accurate...
This paper presents an efficient computational method for time series clustering and application concerning research funding of universities directly under Minster of Education of People Republic of China. Presented approach was based on extraction of trend features with Haar wavelet decomposition from time series data and their use in feature-based agglomerative hierarchical clustering of monthly...
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