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Subspace clustering is one of the active research problem associated with high-dimensional data. Here some of the standard techniques are reviewed to investigate existing methodologies. Although, there have been various forms of research techniques evolved recently, they do not completely mitigate the problems pertaining to noise sustainability and optimization of clustering accuracy. Hence, a novel...
In software projects, there is a data repository which contains the bug reports. These bugs are required to carefully analyse and resolve the problem. Handling these bugs humanly is extremely time consuming process, and it can result the deleying in addressing some important bugs resolutions. To overcome this problem, researchers have introduced many techniques. One of the commonly used algorithm...
Cluster Analysis methods are very important, popular data summarization techniques applied in diverse environments. These techniques retrieve the hidden patterns in large datasets in the form of characterized patterns which can be interpreted further in different contexts. Widespread use of medical information systems and explosive growth of medical databases require traditional manual data analysis...
A fuzzy confusion matrix based cursive handwritten text categorization has been implemented. Printed text is obtained from handwritten text through Modified Optimal Clustering Algorithm (MOCA). Optimal Clustering Algorithm (OCA) groups texts into different subject categories. Learning is conducted to extract the attributes along with corresponding weights for each subjects. Fuzzy confusion matrix...
In this present paper, a Radial Basis Function Network (RBFN) based on Modified Optimal Clustering Algorithm (MOCA) have been developed for clear and occluded fingerprint identification. Unlike conventional OCA technique which only considers intra cluster similarity for performing the desired number of clusters, MOCA combines both intra and inter cluster similarity while grouping such that not only...
FCM is sensitive to initialization and tends to result in local minimum in iterations. This paper studies the crossover and mutation probability of genetic algorithm and presents a new crossover and mutation probability. The proposed clustering scheme based on genetic algorithm and fuzzy c-means takes full advantage of the global optimization of genetic algorithm and the local search ability of FCM...
With the increasing usage of Wi-Fi infrastructure, methods of indoor localization by Wi-Fi are receiving more and more research efforts in the past. Reducing computational complexity and improving the rate of matching effectively can improve accuracy and real-time of localization. In this paper, we propose a novel clustering approach-AP similarity clustering and K-Weighted Nearest Node (KWNN) method...
Feature learning algorithms aim to provide a compact and discriminative representation of complex datasets in order to increase the speed and accuracy of clustering or classification. In this paper, we propose a novel interactive feature learning approach which is mainly based on 3D interactive data visualization and Non-negative Matrix Factorization (NMF). Here, the data is visualized in a 3D interface...
Recently, a fast density peak-based clustering algorithm, namely FDPC, has demonstrated its power on nonspherical clustering problems. In this paper, we propose an enhanced fast density peak-based clustering, namely E-FDPC, for hy-perspectral band selection. The main contributions of the proposed E-FDPC, in comparison with the original FDPC are two folds. First, we introduce a parameter to control...
Local feature matching is one of the most fundamental issues in computer vision. Hierarchical agglomerative clustering (HAC) has been effectively used to distinguish inliers from outliers. The drawback of HAC is its large computational complexity which increases rapidly as the number of feature correspondences increases. To overcome this drawback, this paper proposes a region-constrained feature matching...
The demand of human identification in a non-intrusive manner has risen increasingly in recent years. Several works have already been done in this context using gait-cycle detection from human skeleton data using Microsoft Kinect as a data capture sensor. In this paper we have proposed a novel method for automatic human identification in real time using the fusion of both supervised and unsupervised...
In surveillance and scene awareness applications using power-constrained or battery-powered equipment, performance characteristics of processing hardware must be considered. We describe a novel framework for moving processing platform selection from a single design-time choice to a continuous run-time one, greatly increasing flexibility and responsiveness. Using Histogram of Oriented Gradients (HOG)...
Particle probability hypothesis density filtering has become a tractable means for multi-target tracking due to its capability of handling an unknown and time-varying number of targets in non-linear or non-Gaussian system in the presence of clutter and missing measurements. However, it is time-consuming because hundreds of thousands of particles are required to reach a satisfactory tracking accuracy,...
In cloud computing environment, an application is always composed of several service components. A collection of service components is called a service family, and we name the cloud service components as service family members. In this paper, we propose a solution named Icebreaker to assemble service components belonging to the same application without sniffing tenants' privacy. Icebreaker characterizes...
The paper presents one of the possible modifications of the Projective Adaptive Resonance Theory (PART) clustering algorithm and its application in the processing of text documents. Clustering on the basis of PART can be applied, for example, to generate a dictionary of keywords from a text. The principle of this method is based on clustering of words with the same root word. In order to demonstrate...
This paper analyses how information from user profile influences quality of recommendations. We first start with an overview of recommendation systems, their functions methods used. The empirical part focuses on collaborative filtering method with the aim to find improvement of recommendations based on the user profile. The main objective for realized experiments was to verify the hypothesis that...
We present a parallel hierarchical graph clustering algorithm that uses modularity as clustering criteria to effectively extract community structures in large graphs of different types. In order to process a large complex graph (whose vertex number and edge number are around 1 billion), we design our algorithm based on the Louvain method by investigating graph partitioning and distribution schemes...
With the rapid development of information society, intricate relationship between objects establish huge heterogeneous networks. The linkage is affected by multiple factors, which makes community detection on heterogeneous network a difficult task. Traditional clustering algorithms focus on divided factors, ignoring the combination of them. If the structure of multi-dimensional information is taken...
Multi-instance multi-label learning (MIML) is a framework that addresses label ambiguity when data contains bags, each bag contains instances, and a bag label set is provided for each bag. Instance annotation in the MIML setting is the problem of finding an instance level classifier given training data consisting of labeled bags of instances. Current approaches for instance annotation mainly focus...
On the problem of covert channel detection, the traditional detection algorithms exist specific covert channel blind area, or it is useful for some kind of covert channel detection but ignore other covert channels. In order to solve this problem, in this paper proposes network covert channel analysis method based on the density multilevel two segment clustering. Firstly, the problem of covert channel...
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