The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Data clustering is an important technique in data mining and pattern recognition. In practical tasks the clusters can be of arbitrary shapes. However, many existing algorithms tend to generate only spherical clusters. While density based clustering algorithms are able to deal with arbitrary clusters, they usually involve multiple user-specified parameters. In this paper we propose to solve this problem...
This paper aims to propose a two-stage clustering approach for calibration of traffic flow fundamental diagrams for dynamic traffic assignment (DTA) simulations. Unlike previous research efforts focusing on supervised grouping strategies that are largely dependent on roadway physical attributes, a data-driven perspective is explored using big traffic data. The two-regime modified Greenshields traffic...
Segmentation of cell nuclei is an important step towards automatic analysis of microscopic images. This paper presents an automated technique for nuclear segmentation in skin histopathological images. The proposed technique first detects nuclear seeds using a bank of generalized Laplacian of Gaussian (gLoG) kernels. Based on the detected nuclear seeds, a multi-scale radial line scanning (mRLS) method...
Clustering is a powerful approach for data analysis and its aim is to group objects based on their similarities. Density peaks clustering is a recently introduced clustering method with the advantages of doesn't need any predefined parameters and neither any iterative process. In this paper, a novel density peaks clustering method called IDPC is proposed. The proposed method consists of two major...
This paper introduces a new interpolation method that fills the gap in missing solar filament big data that are captured every day from numerous ground-based and space-based observatories. It proposes a new algorithm that takes two filament event instances and interpolates between them given a cadence. The method combines K-means clustering algorithm, time series shape representation, and linear interpolation...
The fuzzy joint points (FJP) is a method that uses a fuzzy neighborhood notion to deal with neighborhood parameter selection issue of classical density-based clustering and offers an unsupervised clustering tool. Recent works improved the method in terms of speed to enable the method for big data applications. However, space efficiency of the method is still a limiting factor. In this work, we discuss...
In the paper, an efficient spatial clustering algorithm, the improved DBSCAN (Density Based Spatial Clustering of Applications with Noise), is proposed, for cluster analysis of trajectory points gathered from Digital Movies Mobile Playing Systems(DMSs). By searching the density of points which are greater than a given threshold in rural areas, the density-reachable maximum movie playing clusters are...
This paper presents a machine learning approach to explore the phenetic relations of historical scripts and their glyphs. Its first step is the identification of the observable topological transformations in the development of the glyphs, and with the use of these transformations, the method collects the possible cognate glyphs by minimizing the necessary topological transformations between the glyphs...
3D objects learning is a challenging problem in computer vision and digital multimedia due to the wide development of 3D objects scanning technology. Nevertheless, using machine learning for solving such problems is a potential and effective tool. In this paper, we propose a novel approach for 3D objects labeling, it relies on a multi-class boosting algorithm to train the labeling function and spectral...
In a multi-view application, view synthesis is required to synthesize unavailable views using texture and depth information of the given views. In this paper, a compensation algorithm based on template matching for partial recovery of disocclusion regions is proposed. The shape of a template is dynamically adjusted, and a new reference mode which mimics pushing dominoes is also proposed. To find an...
The proliferation of social media has increased the competition among different memes, which can be free texts, trending catchphrases, or micro media. As human attention is limited, these memes compete with each other, and go in and out of popularity at a rapid pace, sometimes even faster than we can recognize. Popular memes often shape the mindsets of online communities, and also shed light on their...
The problem of unsupervised learning is that of trying to find hidden structure in unlabeled data. The last ones confide in, among other things, the choice of the clustering technique. Almost all of the well-known clustering algorithms require input number of clusters which is hard to determine but have a significant influence on the clustering result. Furthermore, the majority is not robust enough...
This paper deals with clustering non-gaussian data with fixed bounds. It considers the problem using recursive mixture estimation algorithms under the Bayesian methodology. Such a solution is often desired in areas, where the assumption of normality of modeled data is rather questionable and brings a series of limitations (e.g., non-negative, bounded data, etc.). Here for modeling the data a mixture...
CFSFDP is a clustering algorithm based on density peaks, which can cluster non-spherical data sets, and also has the advantages of fast clustering and simple realization. However, the global density threshold dc, which leads to the decrease of clustering quality, is specified without the consideration of spatial distribution of the data. Moreover, the data sets with multi-density peaks cannot be clustered...
Data stream is relatively new and emerging domain in the current era of Internet advancement. Clustering data streams is equally important and difficult because of the numerous hurdles attached to it. A number of algorithms have been proposed to offer solutions for efficient clustering. Grid-based clustering approach was adopted few years ago to overcome the limitations of conventional partition-based...
This work proposes a trajectory clustering-based approach for segmenting flow patterns in high density crowd videos. The goal is to produce a pixel-wise segmentation of a video sequence (static camera), where each segment corresponds to a different motion pattern. Unlike previous studies that use only motion vectors, we extract full trajectories so as to capture the complete temporal evolution of...
This study is dealing with the fuzzy c-means clustering combined with entropy maximization and deterministic annealing. The Tsallis entropy is the q-parameter extension of the Shannon entropy. By maximizing the Tsallis entropy within the framework of fuzzy c-means, a statistical mechanical membership function is obtained, which can be applicable to fuzzy clustering. The major issue of this method...
For trajectory model to study mining, using Vector Fields on Manifold instead of the Euclidean distance to metric similarity between trajectories, multi scale transform method is used to optimize the mapping in the Vector Fields on Manifold trajectory distance calculation and use Som algorithm for training a classification model. This method will be the trajectory shape features to measure the similarity...
Clustering is a fundamental and important technique under many circumstances including data mining, pattern recognition, image processing and other industrial applications. During the past decades, many clustering algorithms have been developed, such as DBSCAN, AP and CFS. As the latest clustering algorithm proposed in Science magazine in 2014, clustering by fast search and find of density peaks,...
Traditional methods for image retrieval used metadata associated with images, commonly known as keywords. These methods empowered many World Wide Web (WWW) search engines and achieved reasonable amount of accuracy. A data base shape, color, texture of content based image retrieval (CBIR) and classification algorithm is based on the K-means clustering is proposed in this paper. The algorithm is found...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.