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This paper focuses on the problem of seabed survey mission with multiple target points distributed in large scale area. To complete the survey with AUVs in minimum cost, a two-step procedure using greed strategy is presented. In the first step, the locations of so-called anchor points are found by executing k-means algorithm iteratively. In the second step, an ant-cycle system is used to find out...
Feature selection, as a fundamental component of building robust models, plays an important role in many machine learning and data mining tasks. Since acquiring labeled data is particularly expensive in both time and effort, unsupervised feature selection on unlabeled data has recently gained considerable attention. Without label information, unsupervised feature selection needs alternative criteria...
We propose EC3, a novel algorithm that merges classification and clustering together in order to support both binary and multi-class classification. EC3 is based on a principled combination of multiple classification and multiple clustering methods using a convex optimization function. We additionally propose iEC3, a variant of EC3 that handles imbalanced training data. We perform an extensive experimental...
This paper presents a new method of smart waste city management which makes the environment of the city clean with a low cost. In this approach, the sensor model detects, measures, and transmits waste volume data over the Internet. The collected data including trash bin's geolocation and the serial number is processed by using regression, classification and graph theory. Thenceforth a new method is...
Clustering is an effective method for data analysis and can be exploited to unknown features of data samples, its applications range from data mining to bioinformatics analysis. Several clustering approaches have been proposed in order to obtain a better trade-off between accuracy and efficiency of the clustering process. It is well-known that no existing clustering algorithm completely satisfies...
Clustering results are often affected by covariates that are independent of the clusters one would like to discover. Traditionally, Alternative Clustering algorithms can be used to solve such a problem. However, these suffer from at least one of the following problems: i) continuous covariates or non-linearly separable clusters cannot be handled; ii) assumptions are made about the distribution of...
Power consumption is one of the key optimization objectives for modern integrated circuit designs. More than 40% of the total power consumption is contributed by clock trees due to their high frequency of switching and high capacitance. In the traditional physical design flow, placement is done before clock tree synthesis (CTS). CTS constructs a tree to connect the clock source with all the registers...
With the ever increasing volume of video content, efficient and effective video summarization (VS) techniques are urgently demanded to manage the large amount of video data. Recent developments on sparse representation based approaches have demonstrated promising results for VS. While most existing approaches treat each frame independently, in this paper, the block-sparsity, which means the keyframes...
Bayesian optimization has been demonstrated as an effective methodology for the global optimization. However, it suffers from a computational bottleneck that the inference time grows cubically with the number of observations. In this paper, a Bayesian optimization based on the data-parallel approach is proposed to alleviate this problem. Firstly, an improved geometry motivated clustering algorithm...
Coverage issue in directional sensor networks (DSNs) is different from traditional omni-directional wireless sensor networks (WSNs) due to the limited angle of view and adjustable working direction. This paper sets up a model of monitoring area and a model of sensing area according to the unique characteristics of directional sensor and then derives an optimization model for area coverage ratio maximization...
This survey highlights issues in clustering which hinder in achieving optimal solution or generates inconsistent outputs. We called such malignancies as dark patches. We focus on the issues relating to clustering rather than concepts and techniques of clustering. For better insight into the issues of clustering, we categorize dark patches into three classes and then compare various clustering methods...
Modularity is an evaluation measure for graph clustering. Louvain method is constructed by local optimization for modularity and is bottom up method as well as agglomerative hierarchical clustering. Cluster validity measures are used to evaluate cluster partitions as well as modularity. They are traditional evaluation measures in the field of clustering. We propose a novel graph clustering which is...
Ant colony optimization (ACO) is a quite mature optimization algorithm for combinational problems, but it still attracts many researchers trying to raise its efficiency and/or performance. Some of them endeavor to speed up or improve ACO by choosing more suitable parameters of iteration or update formulas. This work tries to introduce Λ-means clustering to enhance the efficiency of ACO for the traveling...
Efficient utilization of sensor energy to prolong the WSN' lifetime is proposed in this paper. A hybrid PSO Genetic sleep scheduling algorithm for WSNs to escape the local optima trap is the focus of this work. The proposed scheme uses a new parameter: Local Optimum Detector (LOD) for switching from PSO to GA algorithm in order to escape to the local optima trap caused by PSO.
Discriminative clustering has been successfully applied to a number of weakly supervised learning tasks. Such applications include person and action recognition, text-to-video alignment, object co-segmentation and co-localization in videos and images. One drawback of discriminative clustering, however, is its limited scalability. We address this issue and propose an online optimization algorithm based...
This paper is the first to address the problem of unsupervised action localization in videos. Given unlabeled data without bounding box annotations, we propose a novel approach that: 1) Discovers action class labels and 2) Spatio-temporally localizes actions in videos. It begins by computing local video features to apply spectral clustering on a set of unlabeled training videos. For each cluster of...
Dividing a dataset into disjoint groups of homogeneous structure, known as data clustering, constitutes an important problem of data analysis. It can be solved with broad range of methods employing statistical approaches or heuristic procedures. The latter often include mechanisms known from nature as they are known to serve as useful components of effective optimizers. The paper investigates the...
In text mining, document clustering describes the efforts to assign unstructured documents to clusters, which in turn usually refer to topics. Clustering is widely used in science for data retrieval and organisation. In this paper we present a new graph theoretical approach to document clustering and its application on a real-world data set. We will show that the well-known graph partition to stable...
Clustering analysis is an active research branch in the area of data mining due to its simplicity and rapidity. However, K-means algorithm has the shortcomings of heavily depending on the initial clustering center and easily falls into local optimum. In this paper, we consider a deep research on K-means algorithm of optimization. We put forward the first selected initial clustering center of K-means...
This paper considers the optimal energy generation problem for hierarchical system, which consists of multi-cluster power system. In particular, consensus-based distributed hierarchical coordination algorithm is proposed to meet the power generation/demand balance. By using Lagrangian-based approach, we show that the optimization problem for the hierarchical system can be separated into each layer's...
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