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Target tracking is one of the most important and energy killer applications in wireless sensor networks (WSNs). Fault tolerance to any failure is an essential in this context. Hence, we introduce a novel approach toward Base Station (BS) oriented clustering and tracking in WSNs. Proposed method overlooks ad-hoc ability of WSNs to earn energy efficiency and fault tolerance. BS is a powerful energy...
We present an efficient genetic algorithm for mining multi-objective rules from large databases. Multi-objectives will conflict with each other, which makes it optimization problem that is very difficult to solve simultaneously. We propose a multi-objective evolutionary algorithm called improved niched Pareto genetic algorithm(INPGA), which not only accurate selects the candidates but also saves selection...
Clustering is one of the most widely used techniques for exploratory data analysis. Across all disciplines, from social sciences over biology to computer science, people try to get a first intuition about their data by identifying meaningful groups among the data objects. K-means is one of the most famous clustering algorithms. Its simplicity and speed allow it to run on large data sets. However,...
We introduce the questionable observer detection problem: Given a collection of videos of crowds, determine which individuals appear unusually often across the set of videos. The algorithm proposed here detects these individuals by clustering sequences of face images. To provide robustness to sensor noise, facial expression and resolution variations, blur, and intermittent occlusions, we merge similar...
This paper proposes an on-demand soundscape generation and provisioning for a user to experience a real world in a requested remote place. This generation is achieved by spatial audio mixing considering a real world condition like geographical features or townscapes as well as dynamic situation such as town events or weather. The proposed velocity vector-based clustering can reduce the cost of composing/decomposing...
When lines in a power system are constrained, the sensitivity of the power flows on these lines to generator output provides information about how the constraints divide the system and about the ability of sets of generators to increase revenue without increasing dispatch. Clustering is used to identify generators into groups with the potential for market advantage. In this paper, we discuss the implementation...
This paper focuses on document clustering algorithms that build hierarchical solutions. In this paper is evaluate the performance of different criterion functions for the problem of clustering documents.
Researched and developed the methods and non-hierarchical clustering algorithms for determining the optimal initial number of clusters without any background information on the location of the clusters. The methods and algorithms are researched in the famous test set Iris.
A clustering problem with balancing constraints is studied in this paper, which means that the sample number in each cluster has to be at least pre-given value. A modified k-means clustering algorithm is proposed, which adopt the proposed heuristic cluster assignment algorithm to deal with the balancing constraints. Numerical computation shows that the proposed algorithm can deal with the balancing...
This paper is about a few implementation aspects of a clustering algorithm for grouping parameters of the channel's multipath components. Issues as initialization and the computation of distance between multipath components are discussed. Special attention is given to the cluster validation problem dealing with the number of clusters' estimation. In this context, some validation measures and fusion...
Energy efficiency is a major design goal for resource-constraint Wireless Sensor Networks (WSNs). In this paper, an energy efficient clustering approach is employed to meet this design goal. Moreover, given the fact that most of the existing clustering algorithms do not address the hot spot problem that arises in the vicinity of the base station, an unequal clustering mechanism is implemented among...
It is vitally important for applications in detecting DoS attacks, traffic management, and network security to real-time automatically identify traffic patterns in backbone networks with high speed links carrying large numbers of flows. Our objective is to determine traffic patterns that use up a disproportionate fraction of network resources. This paper first analyzes the major time and space cost...
Mobile robot localization is a very important problem in robotics as most robot's tasks need the positional information. Monte Carlo Localization(MCL) is one of the most popular and efficient localization algorithms for mobile robot localization. MCL algorithm represents a robot's pose by a set of weighted particles. In order to further improve the performance of MCL, many extensions have been proposed...
In many real-world applications, the accurate number of clusters in the data set may be unknown in advance. In addition, clustering criteria are usually high dimensional, nonlinear and multi-model functions and most existing clustering algorithms are only able to achieve a clustering solution that locally optimizes them. Therefore, a single clustering criterion sometimes fails to identify all clusters...
Data streams are one of the most challenging environments for machine learning. In many applications, the high volume data streams have an inherent concept drift over time. Identifying novel classes and detecting the occurrence of concept drift in such an environment is a major challenge. In this paper, a new method has been proposed to detect novelty and handle concept drift with limited required...
In this paper, the automatic segmentation of Osteosar-coma in MRI images is formed as a clustering problem. Subsequently, a new dynamic clustering algorithm based on the Harmony Search (HS) hybridized with Fuzzy C-means (FCM) called DCHS is proposed to automatically segment the Osteosarcoma MRI images in an intelligent manner. The concept of variable length in each harmony memory vector is applied...
In this paper, Structure and properties of neural networks with quadratic junction are presented. Unsupervised learning rules about the neural networks are given. Using this kind of neural networks, an ART-based hierarchical clustering algorithm is suggested. The algorithm can determine the number of clusters and clustering data. The time and space complexity of the algorithm are discussed. A 2-D...
Feature selection is an important process in data analysis for information-preserving data reduction. Clustering is inherently a difficult task and is made even more difficult when the selection of relevant features is also an issue. In this paper, we propose an approach for clustering and feature selection simultaneously using a harmony search algorithm. Our approach makes feature selection an integral...
Traffic classification has become a crucial domain of research due to the rise in applications that are either encrypted or tend to change port consecutively. The challenge of flow classification is to determine the applications involved without any information on the payload. In this paper, our goal is to achieve a robust and reliable flow classification using data mining techniques. We propose a...
To minimize energy consumption in the Wireless Sensor Networks (WSNs), we propose a decentralized sensor coordination optimization scheme for Mobile Multi-Target Tracking (MMTT) in WSNs. Our scheme partitions the available sensor-nodes into clusters using the maximum-entropy based clustering criteria. For each tracked target, a number of neighboring clusters are activated based on their Hausdorff...
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